research and development level 1

Building an R&D strategy for modern times

The global investment in research and development (R&D) is staggering. In 2019 alone, organizations around the world spent $2.3 trillion on R&D—the equivalent of roughly 2 percent of global GDP—about half of which came from industry and the remainder from governments and academic institutions. What’s more, that annual investment has been growing at approximately 4 percent per year over the past decade. 1 2.3 trillion on purchasing-power-parity basis; 2019 global R&D funding forecast , Supplement, R&D Magazine, March 2019, rdworldonline.com.

While the pharmaceutical sector garners much attention due to its high R&D spending as a percentage of revenues, a comparison based on industry profits shows that several industries, ranging from high tech to automotive to consumer, are putting more than 20 percent of earnings before interest, taxes, depreciation, and amortization (EBITDA) back into innovation research (Exhibit 1).

What do organizations expect to get in return? At the core, they hope their R&D investments yield the critical technology from which they can develop new products, services, and business models. But for R&D to deliver genuine value, its role must be woven centrally into the organization’s mission. R&D should help to both deliver and shape corporate strategy, so that it develops differentiated offerings for the company’s priority markets and reveals strategic options, highlighting promising ways to reposition the business through new platforms and disruptive breakthroughs.

Yet many enterprises lack an R&D strategy that has the necessary clarity, agility, and conviction to realize the organization’s aspirations. Instead of serving as the company’s innovation engine, R&D ends up isolated from corporate priorities, disconnected from market developments, and out of sync with the speed of business. Amid a growing gap in performance  between those that innovate successfully and those that do not, companies wishing to get ahead and stay ahead of competitors need a robust R&D strategy that makes the most of their innovation investments. Building such a strategy takes three steps: understanding the challenges that often work as barriers to R&D success, choosing the right ingredients for your strategy, and then pressure testing it before enacting it.

Overcoming the barriers to successful R&D

The first step to building an R&D strategy is to understand the four main challenges that modern R&D organizations face:

Innovation cycles are accelerating. The growing reliance on software and the availability of simulation and automation technologies have caused the cost of experimentation to plummet while raising R&D throughput. The pace of corporate innovation is further spurred by the increasing emergence of broadly applicable technologies, such as digital and biotech, from outside the walls of leading industry players.

But incumbent corporations are only one part of the equation. The trillion dollars a year that companies spend on R&D is matched by the public sector. Well-funded start-ups, meanwhile, are developing and rapidly scaling innovations that often threaten to upset established business models or steer industry growth into new areas. Add increasing investor scrutiny of research spending, and the result is rising pressure on R&D leaders to quickly show results for their efforts.

R&D lacks connection to the customer. The R&D group tends to be isolated from the rest of the organization. The complexity of its activities and its specialized lexicon make it difficult for others to understand what the R&D function really does. That sense of working inside a “black box” often exists even within the R&D organization. During a meeting of one large company’s R&D leaders, a significant portion of the discussion focused on simply getting everyone up to speed on what the various divisions were doing, let alone connecting those efforts to the company’s broader goals.

Given the challenges R&D faces in collaborating with other functions, going one step further and connecting with customers becomes all the more difficult. While many organizations pay lip service to customer-centric development, their R&D groups rarely get the opportunity to test products directly with end users. This frequently results in market-back product development that relies on a game of telephone via many intermediaries about what the customers want and need.

Projects have few accountability metrics. R&D groups in most sectors lack effective mechanisms to measure and communicate progress; the pharmaceutical industry, with its standard pipeline for new therapeutics that provides well-understood metrics of progress and valuation implications, is the exception, not the rule. When failure is explained away as experimentation and success is described in terms of patents, rather than profits, corporate leaders find it hard to quantify R&D’s contribution.

Yet proven metrics exist  to effectively measure progress and outcomes. A common challenge we observe at R&D organizations, ranging from automotive to chemical companies, is how to value the contribution of a single component that is a building block of multiple products. One specialty-chemicals company faced this challenge in determining the value of an ingredient it used in its complex formulations. It created categorizations to help develop initial business cases and enable long-term tracking. This allowed pragmatic investment decisions at the start of projects and helped determine the value created after their completion.

Even with outcomes clearly measured, the often-lengthy period between initial investment and finished product can obscure the R&D organization’s performance. Yet, this too can be effectively managed by tracking the overall value and development progress of the pipeline so that the organization can react and, potentially, promptly reorient both the portfolio and individual projects within it.

Incremental projects get priority. Our research indicates that incremental projects account for more than half of an average company’s R&D investment, even though bold bets and aggressive reallocation  of the innovation portfolio deliver higher rates of success. Organizations tend to favor “safe” projects with near-term returns—such as those emerging out of customer requests—that in many cases do little more than maintain existing market share. One consumer-goods company, for example, divided the R&D budget among its business units, whose leaders then used the money to meet their short-term targets rather than the company’s longer-term differentiation and growth objectives.

Focusing innovation solely around the core business may enable a company to coast for a while—until the industry suddenly passes it by. A mindset that views risk as something to be avoided rather than managed can be unwittingly reinforced by how the business case is measured. Transformational projects at one company faced a higher internal-rate-of-return hurdle than incremental R&D, even after the probability of success had been factored into their valuation, reducing their chances of securing funding and tilting the pipeline toward initiatives close to the core.

As organizations mature, innovation-driven growth becomes increasingly important, as their traditional means of organic growth, such as geographic expansion and entry into untapped market segments, diminish. To succeed, they need to develop R&D strategies equipped for the modern era that treat R&D not as a cost center but as the growth engine it can become.

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Choosing the ingredients of a winning r&d strategy.

Given R&D’s role as the innovation driver that advances the corporate agenda, its guiding strategy needs to link board-level priorities with the technologies that are the organization’s focus (Exhibit 2). The R&D strategy must provide clarity and commitment to three central elements: what we want to deliver, what we need to deliver it, and how we will deliver it.

What we want to deliver. To understand what a company wants to and can deliver, the R&D, commercial, and corporate-strategy functions need to collaborate closely, with commercial and corporate-strategy teams anchoring the R&D team on the company’s priorities and the R&D team revealing what is possible. The R&D strategy and the corporate strategy must be in sync while answering questions such as the following: At the highest level, what are the company’s goals? Which of these will require R&D in order to be realized? In short, what is the R&D organization’s purpose?

Bringing the two strategies into alignment is not as easy as it may seem. In some companies, what passes for corporate strategy is merely a five-year business plan. In others, the corporate strategy is detailed but covers only three to five years—too short a time horizon to guide R&D, especially in industries such as pharma or semiconductors where the product-development cycle is much longer than that. To get this first step right, corporate-strategy leaders should actively engage with R&D. That means providing clarity where it is lacking and incorporating R&D feedback that may illuminate opportunities, such as new technologies that unlock growth adjacencies for the company or enable completely new business models.

Secondly, the R&D and commercial functions need to align on core battlegrounds and solutions. Chief technology officers want to be close to and shape the market by delivering innovative solutions that define new levels of customer expectations. Aligning R&D strategy provides a powerful forum for identifying those opportunities by forcing conversations about customer needs and possible solutions that, in many companies, occur only rarely. Just as with the corporate strategy alignment, the commercial and R&D teams need to clearly articulate their aspirations by asking questions such as the following: Which markets will make or break us as a company? What does a winning product or service look like for customers?

When defining these essential battlegrounds, companies should not feel bound by conventional market definitions based on product groups, geographies, or customer segments. One agricultural player instead defined its markets by the challenges customers faced that its solutions could address. For example, drought resistance was a key battleground no matter where in the world it occurred. That framing clarified the R&D–commercial strategy link: if an R&D project could improve drought resistance, it was aligned to the strategy.

The dialogue between the R&D, commercial, and strategy functions cannot stop once the R&D strategy is set. Over time, leaders of all three groups should reexamine the strategic direction and continuously refine target product profiles as customer needs and the competitive landscape evolve.

What we need to deliver it. This part of the R&D strategy determines what capabilities and technologies the R&D organization must have in place to bring the desired solutions to market. The distinction between the two is subtle but important. Simply put, R&D capabilities are the technical abilities to discover, develop, or scale marketable solutions. Capabilities are unlocked by a combination of technologies and assets, and focus on the outcomes. Technologies, however, focus on the inputs—for example, CRISPR is a technology that enables the genome-editing capability.

This delineation protects against the common pitfall of the R&D organization fixating on components of a capability instead of the capability itself—potentially missing the fact that the capability itself has evolved. Consider the dawn of the digital age: in many engineering fields, a historical reliance on talent (human number crunchers) was suddenly replaced by the need for assets (computers). Those who focused on hiring the fastest mathematicians were soon overtaken by rivals who recognized the capability provided by emerging technologies.

The simplest way to identify the needed capabilities is to go through the development processes of priority solutions step by step—what will it take to produce a new product or feature? Being exhaustive is not the point; the goal is to identify high-priority capabilities, not to log standard operating procedures.

Prioritizing capabilities is a critical but often contentious aspect of developing an R&D strategy. For some capabilities, being good is sufficient. For others, being best in class is vital because it enables a faster path to market or the development of a better product than those of competitors. Take computer-aided design (CAD), which is used to design and prototype engineering components in numerous industries, such as aerospace or automotive. While companies in those sectors need that capability, it is unlikely that being the best at it will deliver a meaningful advantage. Furthermore, organizations should strive to anticipate which capabilities will be most important in the future, not what has mattered most to the business historically.

Once capabilities are prioritized, the R&D organization needs to define what being “good” and “the best” at them will mean over the course of the strategy. The bar rises rapidly in many fields. Between 2009 and 2019, the cost of sequencing a genome dropped 150-fold, for example. 2 Kris A. Wetterstrand, “DNA sequencing costs: Data,” NHGRI Genome Sequencing Program (GSP), August 25, 2020, genome.gov. Next, the organization needs to determine how to develop, acquire, or access the needed capabilities. The decision of whether to look internally or externally is crucial. An automatic “we can build it better” mindset diminishes the benefits of specialization and dilutes focus. Additionally, the bias to building everything in-house can cut off or delay access to the best the world has to offer—something that may be essential for high-priority capabilities. At Procter & Gamble, it famously took the clearly articulated aspiration of former CEO A. G. Lafley to break the company’s focus on in-house R&D and set targets for sourcing innovation externally. As R&D organizations increasingly source capabilities externally, finding partners and collaborating with them effectively is becoming a critical capability in its own right.

How we will do it. The choices of operating model and organizational design will ultimately determine how well the R&D strategy is executed. During the strategy’s development, however, the focus should be on enablers that represent cross-cutting skills and ways of working. A strategy for attracting, developing, and retaining talent is one common example.

Another is digital enablement, which today touches nearly every aspect of what the R&D function does. Artificial intelligence can be used at the discovery phase to identify emerging market needs or new uses of existing technology. Automation and advanced analytics approaches to experimentation can enable high throughput screening at a small scale and distinguish the signal from the noise. Digital (“in silico”) simulations are particularly valuable when physical experiments are expensive or dangerous. Collaboration tools are addressing the connectivity challenges common among geographically dispersed project teams. They have become indispensable in bringing together existing collaborators, but the next horizon is to generate the serendipity of chance encounters that are the hallmark of so many innovations.

Testing your R&D strategy

Developing a strategy for the R&D organization entails some unique challenges that other functions do not face. For one, scientists and engineers have to weigh considerations beyond their core expertise, such as customer, market, and economic factors. Stakeholders outside R&D labs, meanwhile, need to understand complex technologies and development processes and think along much longer time horizons than those to which they are accustomed.

For an R&D strategy to be robust and comprehensive enough to serve as a blueprint to guide the organization, it needs to involve stakeholders both inside and outside the R&D group, from leading scientists to chief commercial officers. What’s more, its definition of capabilities, technologies, talent, and assets should become progressively more granular as the strategy is brought to life at deeper levels of the R&D organization. So how can an organization tell if its new strategy passes muster? In our experience, McKinsey’s ten timeless tests of strategy  apply just as well to R&D strategy as to corporate and business-unit strategies. The following two tests are the most important in the R&D context:

• Does the organization’s strategy tap the true source of advantage? Too often, R&D organizations conflate technical necessity (what is needed to develop a solution) with strategic importance (distinctive capabilities that allow an organization to develop a meaningfully better solution than those of their competitors). It is also vital for organizations to regularly review their answers to this question, as capabilities that once provided differentiation can become commoditized and no longer serve as sources of advantage.
• Does the organization’s strategy balance commitment-rich choices with flexibility and learning? R&D strategies may have relatively long time horizons but that does not mean they should be insulated from changes in the outside world and never revisited. Companies should establish technical, regulatory, or other milestones that serve as clear decision points for shifting resources to or away from certain research areas. Such milestones can also help mark progress and gauge whether strategy execution is on track.

Additionally, the R&D strategy should be simply and clearly communicated to other functions within the company and to external stakeholders. To boost its clarity, organizations might try this exercise: distill the strategy into a set of fill-in-the-blank components that define, first, how the world will evolve and how the company plans to refocus accordingly (for example, industry trends that may lead the organization to pursue new target markets or segments); next, the choices the R&D function will make in order to support the company’s new focus (which capabilities will be prioritized and which de-emphasized); and finally, how the R&D team will execute the strategy in terms of concrete actions and milestones. If a company cannot fit the exercise on a single page, it has not sufficiently synthesized the strategy—as the famed physicist Richard Feynman observed, the ultimate test of comprehension is the ability to convey something to others in a simple manner.

Cascading the strategy down through the R&D organization will further reinforce its impact. For example, asking managers to communicate the strategy to their subordinates will deepen their own understanding. A useful corollary is that those hearing the strategy for the first time are introduced to it by their immediate supervisors rather than more distant R&D leaders. One R&D group demonstrated the broad benefits of this communication model: involving employees in developing and communicating the R&D strategy helped it double its Organizational Health Index  strategic clarity score, which measures one of the four “power practices”  highly connected to organizational performance.

R&D represents a massive innovation investment, but as companies confront globalized competition, rapidly changing customer needs, and technological shifts coming from an ever-wider range of fields, they are struggling to deliver on R&D’s full potential. A clearly articulated R&D strategy that supports and informs the corporate strategy is necessary to maximize the innovation investment and long-term company value.

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Start » strategy, what is research and development .

Research and development provides businesses with the information they need to successfully bring their products or services to market.

In any industry, even the most revolutionary products and services are rarely fully conceptualized on day 1. Most often, success in the market stems from extensive, effective research and development (R&D). This is especially true for small businesses, which contribute a significantly higher percentage of sales to R&D work than larger businesses.

Here’s everything you need to know about R&D and why it’s well worth the investment.

What is research and development?

R&D refers to the various activities businesses conduct to prepare new products or services for the marketplace. Businesses of all sizes and sectors can partake in R&D activities, though the amount of investment can vary. For example, technology and health care companies tend to have higher R&D expenses , as do enterprises with larger budgets.

Typically the first step in the development process, R&D is not expected to yield immediate profits. Rather, it focuses on innovation and setting up a company for long-term profitability. During this process, businesses may secure patents, copyrights, and other intellectual property associated with their products and services.

At larger companies, R&D activities are often handled in-house by a designated R&D department. However, some smaller companies may opt to outsource R&D to a third-party research firm, a specialist, or an educational institution.

[Read more: 7 Ways to Find Small Business Grant Opportunities ]

Types of research and development

R&D activities typically fall into one of three main categories:

• Basic research: Basic research, sometimes called fundamental research, aims to provide theoretical insight into specific problems or phenomena. For example, a company looking to develop a new toy for children might conduct basic research into child play development.
• Applied research: This type of research is practical and conducted with a specific goal in mind, most often discovering new solutions for existing problems. The children’s toy company from the previous example might conduct applied research into developing a toy that facilitates play development in a new or improved way.
• Development research: In development research, researchers focus exclusively on applied research to develop new products and improve existing ones. For example, a team of development researchers may test the hypothetical company’s new toy or implement feedback obtained from customers.

Small businesses have limited resources. They don’t have that endless budget that the Fortune 500 company has, which means the small business will have to get creative to conduct worthwhile research and development.

Becca Hoeft, CEO and Founder of Morris Hoeft Group

Why invest in research and development?

While R&D can require a significant investment, it also yields several advantages. Below are four specific areas where your business can benefit by conducting R&D.

New products

R&D supports businesses in developing new offerings or improving existing ones based on market demand. By conducting research and applying your findings to your final product, companies are more likely to develop something that meets customers’ needs and performs well in the marketplace.

R&D can help businesses understand their place in the market as well as identify inefficiencies in their workflows. Insights from R&D activities can illuminate ways to improve operations as well as where to most effectively allocate resources, increasing overall efficiency.

Cost reductions

While developing a well-researched product or service that performs well is likely to maximize profit, R&D aimed at improving internal processes and technologies can reduce the cost of bringing products and services to market.

Businesses that invest in R&D may be eligible for specific tax incentives. For one, the federal R&D tax credit offers a dollar-for-dollar reduction in tax liability for businesses that partake in various research-based activities. Eligible companies can apply for this credit by submitting Form 6765 with their business taxes.

[Read more: How to Seek Funding for Your Invention ]

Overcoming the challenges of small business R&D

According to Becca Hoeft, CEO and Founder of Morris Hoeft Group , small businesses may face numerous challenges related to R&D that their larger counterparts might not experience.

“Small businesses have limited resources,” said Hoeft. “They don’t have that endless budget that the Fortune 500 company has, which means the small business will have to get creative to conduct worthwhile research and development.”

While R&D funding is available through various government grants, university programs, and research institutions, Hoeft noted that it may take some time and strategic planning to obtain it. She recommended that small business owners start talking publicly about what kind of research they are doing and what they need to conduct it.

“Don’t hide under a rock and expect money to magically appear,” Hoeft told CO—. “Get on a stage at a relevant conference [or] start a blog series about your idea.”

Keep in mind that once you start sharing your ideas and what you want to research, “it’s out there in the universe,” said Hoeft. Therefore, protecting your intellectual property before you begin and during the research process is extremely important.

“Ensure your trademarks, patents, and copyrights are in place to protect you and your small business,” Hoeft added.

[Read more: How to Qualify for and Claim the R&D Tax Credit ]

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13.1: An Introduction to Research and Development (R&D)

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Learning Objectives

• Know what constitutes research and development (R&D).
• Understand the importance of R&D to corporations.
• Recognize the role government plays in R&D.

Research and development (R&D) refers to two intertwined processes of research (to identify new knowledge and ideas) and development (turning the ideas into tangible products or processes). Companies undertake R&D in order to develop new products, services, or procedures that will help them grow and expand their operations. Corporate R&D began in the United States with Thomas Edison and the Edison General Electric Company he founded in 1890 (which is today’s GE). Edison is credited with 1,093 patents, but it’s actually his invention of the corporate R&D lab that made all those other inventions possible.Andrea Meyer, “High-Value Innovation: Innovating the Management of Innovation,” Working Knowledge (blog), August 20, 2009, accessed February 22, 2011, http://workingknowledge.com/blog/?p=594 . Edison was the first to bring management discipline to R&D, which enabled a much more powerful method of invention by systematically harnessing the talent of many individuals. Edison’s 1,093 patents had less to do with individual genius and more to do with management genius: creating and managing an R&D lab that could efficiently and effectively crank out new inventions. For fifty years following the early twentieth century, GE was awarded more patents than any other firm in America.Gary Hamel, “The Why, What and How of Management Innovation,” Harvard Business Review , February 2006, accessed February 24, 2011, http://hbr.org/2006/02/the-why-what-and-how-of-management-innovation/ar/1 .

Edison is known as an inventor, but he was also a great innovator. Here’s the difference: an invention brings an idea into tangible reality by embodying it as a product or system. An innovation converts a new idea into revenues and profits. Inventors can get patents on original ideas, but those inventions may not make money. For an invention to become an innovation, people must be willing to buy it in high enough numbers that the firm benefits from making it.A. G. Lafley and Ram Charan, The Game-Changer (New York: Crown Publishing Group, 2008), 21.

Edison wanted his lab to be a commercial success. “Anything that won’t sell, I don’t want to invent. Its sale is proof of utility and utility is success,”A. G. Lafley and Ram Charan, The Game-Changer (New York: Crown Publishing Group, 2008), 25. Edison said. Edison’s lab in Menlo Park, New Jersey, was an applied research lab, which is a lab that develops and commercializes its research findings. As defined by the National Science Foundation, applied research is “systematic study to gain knowledge or understanding necessary to determine the means by which a recognized and specific need may be met.”National Science Foundation, “Definitions of Research and Development,” Office of Management and Budget Circular A-11, accessed March 5, 2011, http://www.nsf.gov/statistics/randdef/fedgov.cfm . In contrast, basic research advances the knowledge of science without an explicit, anticipated commercial outcome.

History and Importance

From Edison’s lab onward, companies learned that a systematic approach to research could provide big competitive advantages. Companies could not only invent new products, but they could also turn those inventions into innovations that launched whole new industries. For example, the radio, wireless communications, and television industry grew out of early-twentieth-century research by General Electric and American Telephone and Telegraph (AT&T, which founded Bell Labs).

The heyday of American R&D labs came in the 1950s and early 1960s, with corporate institutions like Bell Labs, RCA labs, IBM’s research centers, and government institutions such as NASA and DARPA. These labs funded both basic and applied research, giving birth to the transistor, long-distance TV transmission, photovoltaic solar cells, the UNIX operating system, and cellular telephony, each of which led to the creation of not just hundreds of products but whole industries and millions of jobs.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm . DARPA’s creation of the Internet (known at its inception as ARPAnet) and Xerox PARC’s Ethernet and graphical-user interface (GUI) laid the foundations for the PC revolution.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm .

Companies invest in R&D to gain a pipeline of new products. For a high-tech company like Apple, it means coming up with new types of products (e.g., the iPad) as well as newer and better versions of its existing computers and iPhones. For a pharmaceutical company, it means coming out with new drugs to treat diseases. Different parts of the world have different diseases or different forms of known diseases. For example, diabetes in China has a different molecular structure than diabetes elsewhere in the world, and pharmaceutical company Eli Lilly’s new R&D center in Shanghai will focus on this disease variant.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . Even companies that sell only services benefit from innovation and developing new services. For example, MasterCard Global Service started providing customers with emergency cash advances, directions to nearby ATMs, and emergency card replacements.Lance Bettencourt, Service Innovation (New York: McGraw-Hill, 2010), 99.

Innovation also includes new product and service combinations. For example, heavy-equipment manufacturer John Deere created a product and service combination by equipping a GPS into one of its tractors. The GPS keeps the tractor on a parallel path, even under hands-free operation, and keeps the tractor with only a two-centimeter overlap of those parallel lines. This innovation helps a farmer increase the yield of the field and complete passes over the field in the tractor more quickly. The innovation also helps reduce fuel, seed, and chemical costs because there is little overlap and waste of the successive parallel passes.Lance Bettencourt, Service Innovation (New York: McGraw-Hill, 2010), 110.

Did You Know?

Appliance maker Whirlpool has made innovation a strategic priority in order to stay competitive. Whirlpool has an innovation pipeline that currently numbers close to 1,000 new products. On average, Whirlpool introduces one hundred new products to the market each year. “Every month we report pipeline size measured by estimated sales, and our goal this year is $4 billion,” said Moises Norena, director of global innovation at Whirlpool. With Whirlpool’s 2008 revenue totaling $18.9 billion, that means roughly 20 percent of sales would be from new products.Jessie Scanlon, “How Whirlpool Puts New Ideas through the Wringer,” BusinessWeek , August 3, 2009, accessed January 17, 2011, http://www.businessweek.com/innovate/content/aug2009/id2009083_452757.htm .

Not only do companies benefit from investing in R&D, but the nation’s economy benefits as well, as Massachusetts Institute of Technology (MIT) professor Robert Solow discovered. Solow showed mathematically that, in the long run, growth in gross national product per worker is due more to technological progress than to mere capital investment. Solow won a Nobel Prize for his research, and investment in corporate R&D labs grew.

Although R&D has its roots in national interests, it has become globalized. Most US and European Fortune 1000 companies have R&D centers in Asia.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . You’ll see the reasons for the globalization of R&D in Section 13.3 .

The Role of Government

Governments have played a large role in the inception of R&D, mainly to fund research for military applications for war efforts. Today, governments still play a big role in innovation because of their ability to fund R&D. A government can fund R&D directly, by offering grants to universities and research centers or by offering contracts to corporations for performing research in a specific area.

Governments can also provide tax incentives for companies that invest in R&D. Countries vary in the tax incentives that they give to corporations that invest in R&D. By giving corporations a tax credit when they invest in R&D, governments encourage corporations to invest in R&D in their countries. For example, Australia gave a 125 percent tax deduction for R&D expenses. The Australian government’s website noted, “It’s little surprise then, that many companies from around the world are choosing to locate their R&D facilities in Australia.” The government also pointed out that “50 percent of the most innovative companies in Australia are foreign-based.”Committee on Prospering in the Global Economy of the 21st Century (U.S.), Committee on Science, Engineering, and Public Policy (U.S.), Rising Above the Gathering Storm (Washington, DC: National Academies Press, 2007), 195.

Finally, governments can promote innovation through investments in infrastructure that will support new technology and by committing to buy the new technology. China is doing this in a big way, and it is thus influencing the course of many companies around the world. Since 2000, China has had a policy in place “to encourage tech transfer from abroad and to force foreign companies to transfer their R&D operations to China in exchange for access to China’s large volume markets,” reported R&D Magazine in its 2010 review of global R&D.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . For example, any automobile manufacturer that wants to sell cars in China must enter into a partnership with a Chinese company. As a result, General Motors (GM), Daimler, Hyundai, Volkswagen (VW), and Toyota have all formed joint ventures with Chinese companies. General Motors and Volkswagen, for example, have both formed joint ventures with the Chinese company Shanghai Automotive Industry Corporation (SAIC), even though SAIC also sells cars under its own brand.Brian Dumaine, “China Charges into Electric Cars,” Fortune , November 1, 2010, 140. The Chinese government made another strategic decision influencing innovation in the automobile industry. Because no Chinese company is a leader in internal combustion engines, the government decided to leapfrog the technology and focus on becoming a leader in electric cars.Bill Russo, Tao Ke, Edward Tse, and Bill Peng, China’s Next Revolution: Transforming The Global Auto Industry , Booz & Company report, 2010, accessed February 27, 2011, www.booz.com/media/file/China’s_Next_Revolution_en.pdf . “Beijing has pledged that it will do whatever it takes to help the Chinese car industry take the lead in electric vehicles,” notes industry watcher Brian Dumaine. Brian Dumaine, “China Charges into Electric Cars,” Fortune , November 1, 2010, 140. That includes allocating $8 billion in R&D funds as well as another $10 billion in infrastructure (e.g., installing charging stations).Gordon Orr, “Unleashing Innovation in China,” McKinsey Quarterly , January 2011, accessed January 2, 2011, www.mckinseyquarterly.com/Strategy/Innovation/Unleashing_innovation_in_China_2725 . The government will also subsidize the purchase of electric cars by consumers and has committed to buying electric cars for government fleets, thus guaranteeing that there will be buyers for the new electric vehicles that companies invent and develop.

Another role of government is to set high targets that require innovation. In the 1960s, the US Apollo space program launched by President John F. Kennedy inspired US corporations to work toward putting a man on the moon. The government’s investments in the Apollo program sped up the development of computer and communications technology and also led to innovations in fuel cells, water purification, freeze-drying food, and digital image processing now used in medical products for CAT scans and MRIs.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm . Today, government policies coming from the European Union mandate ambitious environmental targets, such as carbon-neutral fuels and energy, which are driving global R&D to achieve environmental goals the way the Apollo program drove R&D in the 1960s.Martin Grueber and Tim Studt, “A Battelle Perspective on Investing in International R&D,” R&D Magazine , December 22, 2009, http://www.rdmag.com/Featured-Articles/2009/12/Global-Funding-Forecast-A-Battelle-Perspective-International-R-D .

After the 1990s, US investment in R&D declined, especially in basic research. Governments in other countries, however, continue to invest. New government-corporate partnerships are developing around the world. IBM, which for years closely guarded its R&D labs (even IBM employees were required to have special badges to enter the R&D area), is now setting up “collaboratories” around the world. These collaboratories are partnerships between IBM researchers and outside experts from government, universities, and even other companies. “The world is our lab now,” says John E. Kelly III, director of IBM Research.Steve Hamm, “How Big Blue Is Forging Cutting-edge Partnerships around the World,” BusinessWeek , August 27, 2009, accessed January 2, 2010, http://www.businessweek.com/print/magazine/content/09_36/b4145040683083.htm . IBM has deals for six future collaboratories in China, Ireland, Taiwan, Switzerland, India, and Saudi Arabia.

The reason for the collaboratory strategy is to share R&D costs—IBM’s partners must share 50 percent of the funding costs, which means that together the partners can participate in a large-scale effort that they’d be hard pressed to fund on their own. An example is IBM’s research partnership with the state-funded Swiss university ETH Zurich. The two are building a $70 million semiconductor lab for nanotech research with the goal of identifying a replacement for the current semiconductor-switch technology.Steve Hamm, “How Big Blue Is Forging Cutting-Edge Partnerships around the World,” BusinessWeek , August 27, 2009, accessed January 2, 2010, http://www.businessweek.com/print/magazine/content/09_36/b4145040683083.htm . Such a breakthrough could harken the creation of a whole new industry.

Of all the countries in the world, the United States remains the largest investor in R&D. One-third of all spending on R&D comes from the United States. Just one government agency—the Department of Defense—provides more funding than all the nations of the world except China and Japan. Nonetheless, other countries are increasing the amounts of money they spend on R&D. Their governments are funding R&D at higher levels and are giving more attractive tax incentives to firms that spend on R&D.

Governments can also play a big role in the protection of intellectual property rights, as you’ll see in Section 13.2 .

KEY TAKEAWAYS

• R&D refers to two intertwined processes of research (to identify new facts and ideas) and development (turning the ideas into tangible products and services.) Companies undertake R&D to get a pipeline of new products. Breakthrough innovations can create whole new industries, which can provide thousands of jobs.
• Invention is the creation of a new idea embodied in a product or process, while innovation takes that new idea and commercializes it in a way that enables a company to generate revenue from it.
• Government support of R&D plays a significant role in innovation. It has been generally accepted that it’s desirable to encourage R&D for reasons of economic growth as well as national security. This has resulted in massive support from public funds for many sorts of laboratories. Governments influence R&D not only by providing direct funding but also by providing tax incentives to companies that invest in R&D. Governments also stimulate innovation through supporting institutions such as education and providing reliable infrastructure.

(AACSB: Reflective Thinking, Analytical Skills)

• What benefits does a company get by investing in R&D?
• Why do organizations make a distinction between basic research and applied research?
• Describe three ways in which government can influence R&D.

Technology Roadmapping and Development pp 447–483 Cite as

Research and Development Project Definition and Portfolio Management

• Olivier L. de Weck 2  
• First Online: 22 June 2022

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Ultimately, technology progresses through individual steps which are the results of specific research and development (R&D) projects. In this chapter, we first describe what kinds of R&D projects exist, and how to plan and successfully execute them. We then consider how multiple projects together – as a set – constitute an R&D portfolio. Portfolios can be defined with the help of targets set by technology roadmaps. Given a fixed total R&D budget, it is also possible to optimize the composition of an R&D portfolio by balancing expected return and risk. We give an example of what an R&D portfolio might look like, by considering the portfolio of a major technology firm.

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The distinction between R&D and R&T is unique to some countries in Europe such as France and Germany, whereas in the United States the term R&D is used throughout. One of the subtleties is that government funding for R&T (projects at TRL 6 or earlier) is generally acceptable, whereas government funding for product and service development (R&D after TRL 6) is generally considered a government subsidy and potentially subject to adverse WTO rulings.

We focus on the “value” generated by technology in Ch. 17. In simple terms, we can think of investing some amount of money in order to improve one (or more) FOM’s by some amount, ∆FOM/∆$, and this improvement in FOM should then later return a positive multiple in terms of enhanced revenues or cost savings, ∆$/∆FOM. The product of these two terms can be interpreted as a ROI of the technology investment.

This happened to the Mars Science Laboratory (MSL) mission which carried the Curiosity rover to the surface of Mars and whose original launch date slipped from 2009 to 2011, in part due to technical challenges with cryogenic actuators.

Source: https://www.airbus.com/innovation/future-technology/autonomy.html

For example, it is usually much more expensive to raise the TRL level of a technology from TRL 5 to 6, compared to raising it from TRL 3 to 4. This is because as technology maturity progresses, the fidelity and complexity of equipment, test procedures, and (simulated or actual) use cases becomes much higher, requiring more time, effort, and money.

The scaled agile framework (SAFe) claims to be able to integrate several projects into a coherent whole at the enterprise level, see: https://www.scaledagileframework.com/

One subtlety of the basic EVM calculations is that it does not capture the interdependencies shown on the critical path diagram (e.g., Fig. 16.5 ), and therefore, the schedule performance in terms of SPI can be different than the schedule tracked in terms of the critical path.

This assumes that the remainder of the project will be executed at the same level of cost efficiency as the project exhibited up until “Time Now.”

A more Machiavellian perspective on overoptimism is that project proponents deliberately low ball project estimates in terms of cost and schedule such that the project is more likely to gain approval and get started. This assumes that, once underway, project leaders will be able to secure additional resources and time as project sponsors will want to see the project succeed, rather than face its cancellation.

An example of such a type of project is the Airbus E-Fan X project wherein the goal was to develop and demonstrate in flight a 2 [MW] class electric propulsion system. The project was set up as an allied partnership between Airbus, Siemens, and Rolls Royce. Note that the project was prematurely stopped due to budget cuts related to the COVID-19 pandemic in 2020.

This is a disguised name to protect the confidentiality of the actual company.

The work in this section is credited to Dr. Kaushik Sinha , mainly done during 2017–2018.

A fundamental assumption for φ min is that even a small investment in a technology may yield value, for example, partnering on an R&D project with external organizations, doing in-depth technology scouting (Ch. 14), modeling and simulation, etc. R&D investments in a technology are usually not “all or nothing” propositions. However, there may be a minimum level of investment needed to “unlock” any value at all.

The details of the individual technologies are not important here, we simply want to illustrate the overall principle of R&D portfolio optimization.

Most technology-based companies, including financial departments led by CFOs, use deterministic planning to allocate resources and are uncomfortable using probabilities or statistical analysis of any sort. This is somewhat surprising, since statistical-based risk analysis is the very basis of financial markets.

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NOAA Readiness Levels

Readiness Levels are a systematic project metric or measurement system that supports assessments of the maturity of R&D projects for transition from research to operation, application, commercial product or service, or other use and allows the consistent comparison of maturity between different types of R&D projects. NOAA’s Policy on Readiness Levels can be found in  NAO 216-115A .

There are 9 Readiness Levels as follows:

Basic research, experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Basic research can be oriented or directed towards some broad fields of general interest, with the explicit goal of a range of future applications (OECD, 2015). 

Applied research, original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Applied research is undertaken either to determine possible uses for the findings of basic research, or to determine new methods or ways of achieving specific and predetermined objectives (OECD, 2015).

Proof-of-concept for system, process, product, service, or tool; this can be considered an early phase of experimental development; feasibility studies may be included.

Successful evaluation of system, subsystem, process, product, service, or tool in a laboratory or other experimental environment; this can be considered an intermediate phase of development.

Successful evaluation of system, subsystem process, product, service, or tool in relevant environment through testing and prototyping; this can be considered the final stage of development before demonstration begins.

Demonstration of a prototype system, subsystem, process, product, service, or tool in relevant or test environment (potential demonstrated).

Prototype system, process, product, service or tool demonstrated in an operational or other relevant environment (functionality demonstrated in near-real world environment; subsystem components fully integrated into system).

Finalized system, process, product, service or tool tested, and shown to operate or  function as expected within user’s environment; user training and documentation completed; operator or user approval given.

System, process, product, service or tool deployed and used routinely.

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• Investing in R&D
• What R&D Offers
• The R&D Tax Credit

Buyouts and Mergers

• R&D Benefits

The Bottom Line

• Business Essentials

Why You Should Invest in Research and Development (R&D)

Research and development (R&D) is the part of a company's operations that seeks knowledge to develop, design, and enhance its products, services, technologies, or processes. Along with creating new products and adding features to old ones, investing in research and development connects various parts of a company's strategy and business plan.

According to the latest Business Enterprise Research and Development survey by the National Center for Science and Engineering and the U.S. Census Bureau, businesses spent $32.5 billion to support their R&D activities in 2020.

Here are some reasons your business should invest in research and development.

Key Takeaways

• Research and development (R&D) is an essential driver of economic growth as it spurs innovation, invention, and progress.
• R&D spending can lead to breakthroughs that can drive profits and well-being for consumers.
• Today, R&D is present in nearly every business sector as companies jockey for position in their respective markets.
• Smaller firms engaged in R&D can offset some of these costs and attract investors thanks to a federal tax break.

Investing in Research and Development (R&D)

The Internal Revenue Service's definition of research and development is investigative activities that a person or business chooses to do with the desired result of a discovery that will create an entirely new product, product line, or service.

However, the activities don't only need to be for disovering new products or services—this is only for tax purposes.

R&D isn’t just about creating new products; it can be used to strengthen an existing product or service with additional features.

Research refers to any new science or thinking that will result in a new product or new features for an existing product. Research can be broken down into either basic research or applied research. Basic research seeks to delve into scientific principles from an academic standpoint, while applied research aims to use that basic research in a real-world setting.

The development portion refers to the actual application of the new science or thinking so that a new or increasingly better product or service can begin to take shape.

Research and development is essentially the first step in developing a new product, but product development is not exclusively research and development. An offshoot of R&D, product development can refer to the entire product life cycle , from conception to sale to renovation to retirement.

R&D Offers Productivity, Product Differentiation

Firms gain a competitive advantage by performing in some way that their rivals cannot easily replicate. If R&D efforts lead to an improved type of business process—cutting marginal costs or increasing marginal productivity—it is easier to outpace competitors.

R&D often leads to a new type of product or service—for example, without research and development, cell phones or other mobile devices would never have been created. The internet, and even how people live today, would be completely different if businesses had not conducted R&D in the past.

Research results give businesses a means to find issues people have and ways to address them, and development allows companies to find unique and different ways to fix the problems.

This leads to many different product and service variations, which gives consumers choices and keeps the markets competitive. Some examples of companies that carry out R&D activities are auto manufacturers, software creators, cutting-edge tech companies, and pharmaceutical firms.

The R&D Tax Credit

In 1981, the IRS started offering tax breaks for companies to spend money and hire employees for research and development. Qualifying companies include startups and other small ventures with qualified research expenses. Such expenses can be used to offset tax liabilities , along with an impressive 20-year carry-forward provision for the credit.

Many entrepreneurs and small businesses have made a large sum of money in a short time by selling good ideas to established firms with many resources. Buyouts are particularly common with online companies, but they can be seen wherever there is a lot of incentive to innovate.

Research and development can help your ideas or business become more attractive to investors and other companies looking to expand.

Advertising and Marketing R&D Benefits

Advertising is full of claims about revolutionary new techniques or never-before-seen products and technologies. Consumers demand new and improved products, sometimes simply because they are new. R&D departments can act as advertising wings in the right market.

R&D strategies let companies create highly effective marketing strategies around releasing a new or existing product with new features. A company can create marketing campaigns to match innovative products and market participation.

What Are the Reasons for R&D?

Research and development keep your business competitive. Without R&D, you risk losing your competitive advantage and falling behind other companies researching and developing new products in your industry.

Why Is R&D Important for Startups?

R&D is essential because it helps you keep your business momentum going. New products and services help you attract more customers, make sales, and give you something to talk about with your investors.

What Factors are Essential in Successful R&D?

Successful research and development depend on many factors, but the most important is a strong interest from your customer base and investors. If you spend money and time researching and developing something no one wants, it's being wasted.

Increased market participation, cost management benefits, advancements in marketing abilities, and trend-matching are all reasons companies invest in R&D. R&D can help a company follow or stay ahead of market trends and keep the company relevant.

Although resources must be allocated to R&D, the innovations gained through this research can actually work to reduce costs through more efficient production processes or more efficient products. R&D efforts can also reduce corporate income tax, thanks to the deductions and credits they generate.

National Center for Science and Engineering. " Businesses Invested $32.5 Billion in Assets to Support Their R&D Activities in the United States in 2020 ."

Tax Foundation. " Reviewing the Federal Tax Treatment of Research & Development Expenses ."

Internal Revenue Service. " About Form 6765, Credit for Increasing Research Activities ."

Internal Revenue Service. " Instructions for Form 3800 (2022) ," Page 2.

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U.S. R&D Increased by $72 Billion in 2021 to $789 Billion; Estimate for 2022 Indicates Further Increase to $886 Billion

January 22, 2024

New data from the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation indicate that research and experimental development (R&D) Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge; directed primarily toward a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes. See Organisation for Economic Co-Operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing: Paris. Available at https://doi.org/10.1787/9789264239012-en ." data-bs-content=" Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge; directed primarily toward a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes. See Organisation for Economic Co-Operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing: Paris. Available at https://doi.org/10.1787/9789264239012-en ." data-endnote-uuid="b45a8ef4-2563-43b7-9759-6f501746099d">​ Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge; directed primarily toward a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes. See Organisation for Economic Co-Operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing: Paris. Available at https://doi.org/10.1787/9789264239012-en . performed in the United States totaled $789.1 billion in 2021 ( table 1 ). The estimated total for 2022, based on performer-reported expectations, is $885.6 billion. Businesses reported a projected $84.1 billion increase in 2022 R&D performance above 2021. (All amounts and calculations are reported in current dollars unless otherwise noted.) Using previous NCSES data, researchers have documented a shift in corporate R&D away from research (basic and applied research combined), Industrial and Corporate Change 18 (1): 1–50 and Arora A, Belenzon S, and Patacconi A. 2018. The Decline of Science in Corporate R&D. Strategic Management Journal 39 (1): 3–32." data-bs-content="For example, see Mowery DC. 2009. Plus ca change: Industrial R&D in the ‘Third Industrial Revolution.’ Industrial and Corporate Change 18 (1): 1–50 and Arora A, Belenzon S, and Patacconi A. 2018. The Decline of Science in Corporate R&D. Strategic Management Journal 39 (1): 3–32." data-endnote-uuid="e03db9b7-a516-464f-a6ed-26109d0e28e7">​ For example, see Mowery DC. 2009. Plus ca change: Industrial R&D in the ‘Third Industrial Revolution.’ Industrial and Corporate Change 18 (1): 1–50 and Arora A, Belenzon S, and Patacconi A. 2018. The Decline of Science in Corporate R&D. Strategic Management Journal 39 (1): 3–32. and noted the relevance of this shift to “policy discussions on the apparent decline in inventiveness and the associated slowdown in productivity growth.” Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review , 111 (3): 871–898." data-bs-content="Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review , 111 (3): 871–898." data-endnote-uuid="2f5d21ec-49e8-40b0-a5db-697e9722b5ee">​ Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review , 111 (3): 871–898. In 2021, businesses funded $130 billion in research, which represented 22% of total business funding for R&D but 49% ​ Percentages in this report are calculated based on unrounded data. of total U.S. funding for research. Businesses performed $609 billion of R&D or 77% of total 2021 U.S. R&D.

The U.S. R&D system consists of the activities of a diverse group of R&D performers and sources of funding. Included here are private businesses, the federal government, nonfederal governments, higher education institutions, and other nonprofit organizations. The organizations that perform R&D often receive significant levels of outside funding, and organizations that fund R&D may also themselves be performers. The data for this InfoBrief derive mainly from NCSES surveys of the annual R&D expenditures of these performers and funders.

U.S. R&D expenditures, by performing sector and source of funding: 2010–22

FFRDC = federally funded research and development center.

a Some data for 2021 are preliminary and may later be revised. b The data for 2022 include estimates and are likely to later be revised. c Federal intramural includes expenditures of federal intramural R&D as well as costs associated with administering extramural R&D.

Data are based on annual reports by performers, except for the nonprofit sector. Expenditure levels for higher education, federal government, and nonfederal government performers are calendar year approximations based on fiscal year data.

National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

The “ Data Sources, Limitations, and Availability ” section at the end of this InfoBrief summarizes the main data sources and methodology and provides further details on the data. Data cited in this report that do not appear in one of this InfoBrief’s tables or figures come from the companion data tables .

Preliminary 2022 Estimates and Current Trends in U.S. R&D Totals and National R&D Intensity

U.s. total r&d.

Year-over-year increases in U.S. total R&D expenditures averaged $19.1 billion (4.1% compound average growth rate [CAGR] ​ All growth rate calculations are reported using compound annual growth rates unless otherwise noted. ) over the 2011–16 period. Beginning with the $50.4 billion increase in 2017–18, subsequent annual increases have been notable including $61.5 billion (2018–19), $51.3 billion (2019–20), and $72.2 billion (2020–21) averaging an 8.6% rate for 2016–21. For 2022, business R&D and total R&D performance are estimated to increase by $84.1 billion and $96.5 billion, respectively.

Annual change in U.S. R&D expenditures and gross domestic product, by performing sectors, 1990–2022

NA = not available.

FFRDCs = federally funded research and development centers

a Some data for 2021 are preliminary and may later be revised. b The R&D data for 2022 include estimates and are likely to later be revised. c Survey data on state internal R&D performance were not available prior to 2006; data for 2008 were not collected.

The longer term trend rates are calculated as compound annual growth rates.

Adjusting for inflation, https://www.bea.gov/iTable/index_nipa.cfm . Note that GDP deflators are calculated on an economy-wide scale and do not explicitly focus on R&D." data-bs-content="In this report, dollars adjusted for inflation (i.e., constant dollars) are based on the gross domestic product (GDP) implicit price deflator (currently in 2017 dollars) as published by the Bureau of Economic Analysis (BEA) at https://www.bea.gov/iTable/index_nipa.cfm . Note that GDP deflators are calculated on an economy-wide scale and do not explicitly focus on R&D." data-endnote-uuid="63d13dcc-2827-49c6-8bfd-d871926442ed">​ In this report, dollars adjusted for inflation (i.e., constant dollars) are based on the gross domestic product (GDP) implicit price deflator (currently in 2017 dollars) as published by the Bureau of Economic Analysis (BEA) at https://www.bea.gov/iTable/index_nipa.cfm . Note that GDP deflators are calculated on an economy-wide scale and do not explicitly focus on R&D. growth in U.S. total R&D averaged 4.4% annually over the 2011–21 period. By comparison, average annual growth of U.S. total R&D in the prior decade (2001–11) was lower at 2.2%. The estimate for 2022 shows inflation-adjusted R&D growing at 4.8% from the 2021 level. Comparisons in constant dollars demonstrate the effect of the recent inflationary episode https://www.minneapolisfed.org/about-us/monetary-policy/inflation-calculator/consumer-price-index-1913- ). While the CPI is a more commonly known inflation measure, as noted above and in accordance with international standards for R&D reporting, dollars in this report are adjusted for inflation using the GDP implicit price deflator." data-bs-content="Inflation measured by the Consumer Price Index (CPI) for 2014–20 ranged between 0.1% and 2.4%. Inflation was 4.7% and 8.0% in 2021 and 2022, respectively ( https://www.minneapolisfed.org/about-us/monetary-policy/inflation-calculator/consumer-price-index-1913- ). While the CPI is a more commonly known inflation measure, as noted above and in accordance with international standards for R&D reporting, dollars in this report are adjusted for inflation using the GDP implicit price deflator." data-endnote-uuid="87fcfa72-54a5-4525-9222-5ccb1277bfe3">​ Inflation measured by the Consumer Price Index (CPI) for 2014–20 ranged between 0.1% and 2.4%. Inflation was 4.7% and 8.0% in 2021 and 2022, respectively ( https://www.minneapolisfed.org/about-us/monetary-policy/inflation-calculator/consumer-price-index-1913- ). While the CPI is a more commonly known inflation measure, as noted above and in accordance with international standards for R&D reporting, dollars in this report are adjusted for inflation using the GDP implicit price deflator. on real R&D performance. In constant dollar terms, business R&D performance is estimated to increase by $35.0 billion over the 2021 level. Federal intramural R&D decreased in 2021 from the prior year total, but the estimated increase in 2022 (based on FY 2022 obligations and FY 2023 projections for federal intramural R&D), offsets the 2021 decline. For federally funded research and development centers (FFRDCs), nonfederal governments, and universities, the constant value of R&D performance is estimated to decline in 2022 ( table 2 , figure 1 ). For nonprofit organizations, the change in 2022 R&D is not statistically significant.

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Year-over-year changes in U.S. R&D expenditures, by performing sector: 2014–22

Some data for 2021 are preliminary and may later be revised. The data for 2022 include estimates and are likely to later be revised. Annual changes in nonfederal government R&D expenditures are included in the "All performing sectors" category but not shown separately because they are less than $0.1 billion.

R&D-to-GDP Ratio

The ratio of total national R&D expenditures to gross domestic product (GDP) (i.e., R&D intensity) is widely used by national statistical offices and other policy analysts as an overall gauge of the relative priority of a nation’s R&D effort among multiple investment and consumption options. In this edition of the National Patterns series, the ratio of U.S. R&D to GDP was 3.34% in 2021 and is estimated to be 3.44% in 2022 ( figure 2 ). Prior to 2019 when R&D intensity reached 3.09%, the highest U.S. ratios recorded were 2.79% in 1964, 2.78% in 2009, 2.77% again in 2016, 2.82% in 2017, and 2.92% in 2018. ​ Due to sample variability in the data for the business R&D component, the calculated R&D-to-GDP ratios for 1964, 2009, and 2017 are not significantly different from one another at a 90% confidence level. Additionally, non-U.S. R&D-to-GDP ratios are adjusted for net R&D capital accumulation. Reaching an R&D intensity level above 3.0% is widely regarded in the R&D policy community as a notable national achievement. The U.S. 2021 R&D to GDP ratio exceeded the Organisation for Economic Co-operation and Development average (2.72%). The U.S. ratio also exceeded that of other key R&D-performing nations, such as China (2.43%), France (2.22%), and the United Kingdom (2.91% [provisional]). Israel (5.56%) and South Korea (4.93%) had higher ratios than the United States, whereas Germany (3.13%) and Japan (3.30%) had similar ratios to the United States. https://www.oecd.org/sti/msti.htm ." data-bs-content="See Organisation for Economic Co-Operation and Development, OECD Main Science and Technology Indicators Database, September 2023. Available at https://www.oecd.org/sti/msti.htm ." data-endnote-uuid="8c31a40d-29ce-4866-a5ed-492aaa3eab66">​ See Organisation for Economic Co-Operation and Development, OECD Main Science and Technology Indicators Database, September 2023. Available at https://www.oecd.org/sti/msti.htm .

Ratio of U.S. R&D to GDP, by source of funds for R&D: 1953–2022

GDP = gross domestic product

Some data for 2021 are preliminary and may later be revised. The data for 2022 include estimates and are likely to later be revised. The federally funded data represent the federal government as a funder of R&D by all performers; similarly the business funded data cover the business sector as a funder of R&D by all performers. The "other" category includes the R&D funded by all other sources—mainly, by higher education, nonfederal government, and nonprofit organizations. The gross domestic product data used reflect the U.S. Bureau of Economic Analysis statistics of late October 2023.

The extent to which the rising ratio of U.S. R&D to GDP is attributable to increased business funding of R&D is clear. Over the past decade (2011–21), business funding grew at an 8.3% rate while federal funding grew at a 1.5% rate and GDP grew at a 4.2% rate. Notably, the higher education sector’s funding of R&D grew at 6.1% over the same period.

Federally funded R&D as a percentage of GDP peaked in the 1960s at 1.86% in 1964 and generally has declined since. Since 2014, federal funding for R&D has remained at or below 0.70% of GDP. By contrast, business R&D funding in 2010 was 1.65% of GDP and increased to 2.50% by 2021.

Performers of R&D

The business sector is by far the largest performer of U.S. R&D. In 2021, domestically performed business R&D accounted for $608.6 billion, or 77% of the $789.1 billion national R&D total ( table 1 and table 3 ). The business sector’s predominance in national R&D performance has long been the case, with its annual share ranging between 69% and 77% since 2000.

Sales, R&D, R&D intensity, and employment for companies that performed or funded business R&D in the United States, by selected industry and company size: 2021

NAICS = North American Industry Classification System.

a Dollar values are for goods sold or services rendered by R&D-performing or R&D-funding companies located in the United States to customers outside of the company, including the U.S. federal government, foreign customers, and the company's foreign subsidiaries. Included are revenues from a company’s foreign operations and subsidiaries and from discontinued operations. If a respondent company is owned by a foreign parent company, sales to the parent company and to affiliates not owned by the respondent company are included. Excluded are intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes. b All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. c R&D intensity is the cost of domestic R&D paid for by the respondent company and others outside of the company and performed by the company divided by domestic net sales of companies that performed or funded R&D. d Data recorded on 12 March represent employment figures for the year. Total employment at companies that performed or funded R&D. e Headcounts of researchers, R&D managers, technicians, clerical staff, and others assigned to R&D groups. f Only selected (NAICS 42, 51, 5413, 5415, 5417) nonmanufacturing sectors are sampled for the 1–9 employee population in the Annual Business Survey. Based on prior survey results, businesses with 1–9 employees in other nonmanufacturing subsectors are not believed to perform substantial amounts of R&D.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned.

National Center for Science and Engineering Statistics and Census Bureau, Annual Business Survey, Business Enterprise Research and Development Survey, 2021.

R&D performed in the United States by businesses occurs widely in manufacturing and nonmanufacturing. In 2021, manufacturing companies of all sizes (1 employee to more than 25,000 employees) performed 53.7% of all business R&D ( table 3 ). By contrast, microbusinesses (1–9 employees) in manufacturing industries account for just 12.2% of R&D performed by companies with fewer than 10 employees. The R&D sales intensity (42.5%) and R&D employment intensity (62.8%) are both greater for microbusinesses than for other businesses. Information (NAICS 51), ​ North American Industry Classification System (NAICS). including software publishing (5112), Computer systems design and related services (NAICS 5415), and scientific research and development services (NAICS 5417) account for 73.5% of nonmanufacturing industry R&D. https://ncses.nsf.gov/surveys/annual-business-survey/ and https://ncses.nsf.gov/surveys/business-enterprise-research-development/ . See also: Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops \$600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 . Kindlon A; National Center for Science and Engineering Statistics (NCSES). 2023. Microbusinesses Performed \$6.1 Billion of R&D in the United States in 2021. NSF 24-302. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24302 ." data-bs-content="Additional statistics on R&D performed in the United States by the business sector are available at https://ncses.nsf.gov/surveys/annual-business-survey/ and https://ncses.nsf.gov/surveys/business-enterprise-research-development/ . See also: Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops \$600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 . Kindlon A; National Center for Science and Engineering Statistics (NCSES). 2023. Microbusinesses Performed \$6.1 Billion of R&D in the United States in 2021. NSF 24-302. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24302 ." data-endnote-uuid="53a042de-c5f1-4c3d-8cfc-b710c36f84ca">​ Additional statistics on R&D performed in the United States by the business sector are available at https://ncses.nsf.gov/surveys/annual-business-survey/ and https://ncses.nsf.gov/surveys/business-enterprise-research-development/ . See also: Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops $600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 . Kindlon A; National Center for Science and Engineering Statistics (NCSES). 2023. Microbusinesses Performed $6.1 Billion of R&D in the United States in 2021. NSF 24-302. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24302 .

U.S. R&D expenditures, by performing sector, source of funds, and type of R&D: 2021

* = amount < $0.5 million; ** = small to negligible amount, included as part of the funding provided by nonprofit organizations.

Some data for 2021 are preliminary and may later be revised.

Higher Education

R&D performed in the United States by the higher education sector totaled $85.8 billion in 2021, or 11% of U.S. total R&D ( table 1 and table 4 ). National Patterns differ from the underlying survey data in several respects. First, National Patterns translates the Higher Education R&D (HERD) Survey’s primary data in academic fiscal years to calendar year equivalents. Second, National Patterns reports higher education R&D expenditures that are adjusted to remove the double-counting of pass-through funding included in HERD Survey source data. For further details on this topic, see “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes ." data-bs-content="The data on higher education R&D reported by National Patterns differ from the underlying survey data in several respects. First, National Patterns translates the Higher Education R&D (HERD) Survey’s primary data in academic fiscal years to calendar year equivalents. Second, National Patterns reports higher education R&D expenditures that are adjusted to remove the double-counting of pass-through funding included in HERD Survey source data. For further details on this topic, see “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes ." data-endnote-uuid="55048a01-21d0-4e31-8840-510c24784cd4">​ The data on higher education R&D reported by National Patterns differ from the underlying survey data in several respects. First, National Patterns translates the Higher Education R&D (HERD) Survey’s primary data in academic fiscal years to calendar year equivalents. Second, National Patterns reports higher education R&D expenditures that are adjusted to remove the double-counting of pass-through funding included in HERD Survey source data. For further details on this topic, see “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes . In the period 2000–21, the higher education share of U.S. total R&D ranged between 11% and 14%.

Adjusted for inflation, growth in this sector’s R&D performance averaged 1.7% annually during 2011–21, well behind U.S. total R&D growth (4.4%). For the preceding decade, growth in higher education R&D performance was a robust 4.1%. The annual percent change in 2010–20 varied; there was low growth or contraction in 2010–14 with a return to modest increases in 2015–20. The estimate for 2022 indicates a slight contraction (-0.4%) when measured in constant dollars as inflation outpaced an increase in the level of higher education R&D performance ( table 2 ).

Federal Agencies and Federally Funded Research and Development Centers

The federal government performed $66.8 billion of the U.S. R&D total in 2021 ( table 1 and table 3 ). This amount included $41.5 billion (5% of the U.S. total) performed by the intramural R&D facilities of federal agencies and $25.3 billion (3%) performed by the 43 federally funded research and development centers (FFRDCs). ​ The number of FFRDCs reflects that NCSES was informed in June 2021 that the Green Bank Observatory separated from the National Radio Astronomy Observatory in October 2016 to become an independent institution; both retained FFRDC status. The Master Government List of FFRDCs was subsequently updated to reflect this change. The federal share of U.S. R&D performance ranged between 11% and 13% in 2001–11. Subsequently, the federal share is estimated to decline to 8% in 2022. Measured in constant dollars, federal R&D performance is estimated to increase in 2022 after a modest decline in 2021 ( table 2 ).

State Government

State agency intramural R&D performance in 2021 totaled $685 million—a small share (about 0.1%) of the U.S. total ( table 1 and table 4 ). This includes all 50 states and the District of Columbia.

Nonprofit Organizations

R&D performed in the United States by nonprofit organizations (excluding higher education institutions and federal and nonfederal government) was $27.2 billion in 2021 ( table 1 and table 4 ). ​ The most recent data on nonprofit organization R&D come from the FY 2021 Nonprofit Research Activities (NPRA) module of the ABS and the 2016 NPRA Survey. Data for nonprofit organization R&D, 2017–19 are estimated based on the 2016 and 2020 data as revised in the 2021 survey. The availability of NPRA survey data allowed for improved measurement of nonprofit R&D performance over the 2017–22 period, resulting in minor changes to previously published estimates. For 1998–2015, data for nonprofit organization R&D funded by the federal government come from the NCSES annual Survey of Federal Funds for Research and Development; data for that funded by businesses and by the nonprofit sector itself are estimated, based on parameters from the 1996–97 Survey of Research and Development Funding and Performance by Nonprofit Organizations. This was 3% of U.S. total R&D, a share that has changed little since the early 2000s.

R&D by Type of R&D

In 2021, basic research activities in all sectors accounted for $118.6 billion, or 15% of U.S. total R&D expenditures ( table 5 ). Applied research was $144.0 billion, or 18% of the total. Most of the total of U.S. R&D expenditures was experimental development at $526.4 billion, or 67%.

U.S. R&D expenditures, by type of R&D: Selected years, 1970–2022

a Some data for 2021 are preliminary and may later be revised. a The data for 2022 include estimates and are likely to later be revised.

Data throughout the span of time reported here are consistently based on Organisation for Economic Co-operation and Development Frascati Manual definitions for basic research, applied research, and experimental development. Prior to 2010, however, some changes had been introduced in the questionnaires of the sectoral expenditure surveys to improve the accuracy of respondents' classification of their R&D by type. Accordingly, small percentage changes in the historical data may not be meaningful.

The higher education sector accounted for just under half (46%) of basic research performance in 2021 ( table 5 ). The business sector was the second-largest basic research performer (34%). Business was the majority performer (62%) of the $144.0 billion of applied research in 2021; higher education was second at 16%. Federal intramural performers plus FFRDCs accounted for 15% of the applied research total. Business continued to dominate development performance, accounting for 91% of the U.S. total $526.4 billion of that category in 2021.

Federal funding accounted for 40% of the $118.6 billion of basic research in 2021 ( table 4 ). National Patterns of R&D uses the general term “estimates” to describe survey estimates, modeled estimates, and projections. Results that combine these techniques are also called estimates because survey estimates are their major component." data-bs-content="Estimates of the type of R&D by source of funding are based on survey responses for federal funding by type of R&D and modeled using nonfederal funding sources of total R&D and the total nonfederally funded R&D by type. Because of this estimation procedure, comparisons of R&D type by funding source are not supported by statistical testing. National Patterns of R&D uses the general term “estimates” to describe survey estimates, modeled estimates, and projections. Results that combine these techniques are also called estimates because survey estimates are their major component." data-endnote-uuid="928500d8-f524-42db-8ab0-16ee23aaedc4">​ Estimates of the type of R&D by source of funding are based on survey responses for federal funding by type of R&D and modeled using nonfederal funding sources of total R&D and the total nonfederally funded R&D by type. Because of this estimation procedure, comparisons of R&D type by funding source are not supported by statistical testing. National Patterns of R&D uses the general term “estimates” to describe survey estimates, modeled estimates, and projections. Results that combine these techniques are also called estimates because survey estimates are their major component. But federal funds were less prominent for applied research (29% of $144.0 billion) and experimental development (11% of $526.4 billion). The business sector provided the greatest share of funding for applied research (61%) and the predominant share for experimental development (88%). Notably, it also accounted for a sizable share (36%) of funding for basic research.

Over the 2011–21 period, the split of U.S. total R&D expenditures among the three types of R&D did not largely change. The share of applied research ranged between 18% and 21% throughout the period ( table 5 ). Similarly, the share of basic research remained in the 15%–17% range. Experimental development’s share ranged between 62% and 67%. Adjusting for inflation, about $27 billion more in basic research was performed in 2021 than in 2011, $41 billion more in applied research, and $182 billion more in experimental development.

U.S. research expenditures, by source of funds: 2011 and 2021

Figures for 2021 are preliminary and may later be revised. Other nonfederal includes nonfederal government, higher education, nonprofit organizations.

Social scientists have noted important differences in the nature, role, and impact of research (basic and applied combined) and experimental development. American Economic Review 111 (3): 871–898 or Mezzanotti F and Simcoe T; National Bureau of Economic Research. 2023. Research and/or Development? Financial Frictions and Innovation Investment . Working Paper No. 31521." data-bs-content="For example, see Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review 111 (3): 871–898 or Mezzanotti F and Simcoe T; National Bureau of Economic Research. 2023. Research and/or Development? Financial Frictions and Innovation Investment . Working Paper No. 31521." data-endnote-uuid="bd9f6f04-1435-4ecc-96bd-4195512ef672">​ For example, see Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review 111 (3): 871–898 or Mezzanotti F and Simcoe T; National Bureau of Economic Research. 2023. Research and/or Development? Financial Frictions and Innovation Investment . Working Paper No. 31521. Additionally, the shifting in the relative roles of performers and funders by sector—particularly among business, government, and higher education—is of great interest ( table 6 , figure 3 ). In 2021, business expenditures on R&D performed by domestic businesses, higher education institutions, governments, and nonprofit organizations totaled $591.0 billion, divided between $461.0 billion (78%) on experimental development and $130.0 billion (22%) on research ( table 7 ). These business expenditures on research funded 49% of total U.S. research in 2021, up from 38% in 2011 ( figure 3 ). Over the same period, the federally funded share of U.S. total research declined from 44% in 2011 to 34% in 2021. Comparably, the federally funded share of basic research fell from 53% in 2011 to 40% in 2021.

R&D performance also demonstrates the enhanced role of business in the domestic research system. In 2011, businesses performed 18% of U.S. basic research and 39% of total research, but the sector’s share of basic and total research rose to 34% and 49%, respectively, by 2021. The share of U.S. basic research performed by higher education institutions—historically, the nation’s largest basic research performer—declined from 54% in 2011 to 46% in 2021. In absolute terms, higher education basic research performance increased from $40 billion to $54 billion during this period. The increased relative role of the business sector as a funder and performer of basic and applied research is remarkable.

U.S. R&D expenditures by type of R&D and source of funds: Selected years, 1970–2022

a Some data for 2021 are preliminary and may later be revised. b The R&D data for 2022 include estimates and are likely to later be revised.

Other nonfederal includes nonfederal government, higher education, and nonprofit organizations.

U.S. business R&D expenditures, by type of R&D: 1970–2022

Data sources, limitations, and availability.

The statistics on U.S. R&D presented in this report derive mainly from integrating the data on R&D expenditures and funding collected by NCSES’s annual national surveys of the organizations that perform and fund the vast majority of U.S. R&D. These surveys cover each of four sectors of the economy: higher education, government, business enterprise, and nonprofit organizations. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development ( https://doi.org/10.1787/9789264239012-en ). " data-bs-content="For further details on the correspondence between sectors used to measure R&D and those used in the System of National Accounts, please see the Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development ( https://doi.org/10.1787/9789264239012-en ). " data-endnote-uuid="fcd52a9b-ea9c-4330-ab0f-229d8ab01d7f">​ For further details on the correspondence between sectors used to measure R&D and those used in the System of National Accounts, please see the Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development ( https://doi.org/10.1787/9789264239012-en ). In some cases, the primary data from these surveys are adjusted to enable consistent integration of the statistics across these separately conducted surveys. The 2022 business R&D data is based on respondents’ projected R&D costs and will be revised when actual R&D costs are collected in the following year. In addition, preliminary or otherwise estimated values may be used where final data from one or more of the surveys are not yet available but can reasonably be estimated. Estimates in this InfoBrief are based on census and sample survey data which are subject to nonsampling error. Sample-survey–based estimates are also subject to sampling error. All comparative statements in this InfoBrief have undergone statistical testing and are significant at the 90% confidence level except statements reliant on modeled estimates.

The R&D surveys include NCSES’s annual surveys of business R&D (the Business Enterprise Research and Development Survey for 2019–21, the preceding Business Research and Development Survey for 2017–18, the Business R&D and Innovation Survey for 2008–16, and the Survey of Industrial R&D for 2007 and earlier years). In addition, the business R&D totals include the R&D expenditures reported by “micro” companies (defined as companies with fewer than 10 employees) through NCSES surveys fielded for 2016 and forward (the 2016 Business R&D and Innovation Survey—Microbusiness and the Annual Business Survey (ABS) since 2017). National Patterns tabulations, see the “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes ." data-bs-content="Estimates from the NCSES business and nonprofit organization R&D surveys mentioned are all derived from sample data and thereby contain sampling error. Consequently, estimates of total U.S. R&D also contain sampling error. For more information on this topic and other surveys used in the National Patterns tabulations, see the “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes ." data-endnote-uuid="5f438f41-1d12-416e-9a24-5c15964d76ef">​ Estimates from the NCSES business and nonprofit organization R&D surveys mentioned are all derived from sample data and thereby contain sampling error. Consequently, estimates of total U.S. R&D also contain sampling error. For more information on this topic and other surveys used in the National Patterns tabulations, see the “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes . Other NCSES survey data sources are the Higher Education Research and Development Survey (for FYs 2010–20), the preceding Survey of R&D Expenditures at Universities and Colleges (FY 2009 and earlier years), the Survey of Federal Funds for Research and Development (FYs 2020–21 and earlier years), and the FFRDC Research and Development Survey (FY 2020 and earlier years). Amounts for the R&D performed by nonprofit organizations with funding from the nonprofit sector and from business sources are estimated based on data and parameters from the FY 2021 Nonprofit Research Activities (NPRA) module of the ABS, the 2016 NPRA Survey, and the 1996–97 Survey of R&D Funding and Performance by Nonprofit Organizations.

A full set of detailed statistical tables and methodology information for the National Patterns data are available at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022 . For further information and questions, contact the author.

1 Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge; directed primarily toward a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes. See Organisation for Economic Co-Operation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing: Paris. Available at https://doi.org/10.1787/9789264239012-en .

2 For example, see Mowery DC. 2009. Plus ca change: Industrial R&D in the ‘Third Industrial Revolution.’ Industrial and Corporate Change 18 (1): 1–50 and Arora A, Belenzon S, and Patacconi A. 2018. The Decline of Science in Corporate R&D. Strategic Management Journal 39 (1): 3–32.

3 Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review , 111 (3): 871–898.

4 Percentages in this report are calculated based on unrounded data.

5 All growth rate calculations are reported using compound annual growth rates unless otherwise noted.

6 In this report, dollars adjusted for inflation (i.e., constant dollars) are based on the gross domestic product (GDP) implicit price deflator (currently in 2017 dollars) as published by the Bureau of Economic Analysis (BEA) at https://www.bea.gov/iTable/index_nipa.cfm . Note that GDP deflators are calculated on an economy-wide scale and do not explicitly focus on R&D.

7 Inflation measured by the Consumer Price Index (CPI) for 2014–20 ranged between 0.1% and 2.4%. Inflation was 4.7% and 8.0% in 2021 and 2022, respectively ( https://www.minneapolisfed.org/about-us/monetary-policy/inflation-calculator/consumer-price-index-1913- ). While the CPI is a more commonly known inflation measure, as noted above and in accordance with international standards for R&D reporting, dollars in this report are adjusted for inflation using the GDP implicit price deflator.

8 Due to sample variability in the data for the business R&D component, the calculated R&D-to-GDP ratios for 1964, 2009, and 2017 are not significantly different from one another at a 90% confidence level. Additionally, non-U.S. R&D-to-GDP ratios are adjusted for net R&D capital accumulation.

9 See Organisation for Economic Co-Operation and Development, OECD Main Science and Technology Indicators Database, September 2023. Available at https://www.oecd.org/sti/msti.htm .

10 North American Industry Classification System (NAICS).

11 Additional statistics on R&D performed in the United States by the business sector are available at https://ncses.nsf.gov/surveys/annual-business-survey/ and https://ncses.nsf.gov/surveys/business-enterprise-research-development/ . See also: Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops $600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 . Kindlon A; National Center for Science and Engineering Statistics (NCSES). 2023. Microbusinesses Performed $6.1 Billion of R&D in the United States in 2021. NSF 24-302. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24302 .

12 The data on higher education R&D reported by National Patterns differ from the underlying survey data in several respects. First, National Patterns translates the Higher Education R&D (HERD) Survey’s primary data in academic fiscal years to calendar year equivalents. Second, National Patterns reports higher education R&D expenditures that are adjusted to remove the double-counting of pass-through funding included in HERD Survey source data. For further details on this topic, see “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes .

13 The number of FFRDCs reflects that NCSES was informed in June 2021 that the Green Bank Observatory separated from the National Radio Astronomy Observatory in October 2016 to become an independent institution; both retained FFRDC status. The Master Government List of FFRDCs was subsequently updated to reflect this change.

14 The most recent data on nonprofit organization R&D come from the FY 2021 Nonprofit Research Activities (NPRA) module of the ABS and the 2016 NPRA Survey. Data for nonprofit organization R&D, 2017–19 are estimated based on the 2016 and 2020 data as revised in the 2021 survey. The availability of NPRA survey data allowed for improved measurement of nonprofit R&D performance over the 2017–22 period, resulting in minor changes to previously published estimates. For 1998–2015, data for nonprofit organization R&D funded by the federal government come from the NCSES annual Survey of Federal Funds for Research and Development; data for that funded by businesses and by the nonprofit sector itself are estimated, based on parameters from the 1996–97 Survey of Research and Development Funding and Performance by Nonprofit Organizations.

15 Estimates of the type of R&D by source of funding are based on survey responses for federal funding by type of R&D and modeled using nonfederal funding sources of total R&D and the total nonfederally funded R&D by type. Because of this estimation procedure, comparisons of R&D type by funding source are not supported by statistical testing. National Patterns of R&D uses the general term “estimates” to describe survey estimates, modeled estimates, and projections. Results that combine these techniques are also called estimates because survey estimates are their major component.

16 For example, see Arora A, Belenzon S, and Sheer L. 2021. Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review 111 (3): 871–898 or Mezzanotti F and Simcoe T; National Bureau of Economic Research. 2023. Research and/or Development? Financial Frictions and Innovation Investment . Working Paper No. 31521.

17 For further details on the correspondence between sectors used to measure R&D and those used in the System of National Accounts, please see the Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development ( https://doi.org/10.1787/9789264239012-en ).

18 Estimates from the NCSES business and nonprofit organization R&D surveys mentioned are all derived from sample data and thereby contain sampling error. Consequently, estimates of total U.S. R&D also contain sampling error. For more information on this topic and other surveys used in the National Patterns tabulations, see the “Technical Notes” at https://ncses.nsf.gov/data-collections/national-patterns/2021-2022#technical-notes .

Suggested Citation

Anderson G; National Center for Science and Engineering Statistics (NCSES). 2024. U.S. R&D Increased by $72 Billion in 2021 to $789 Billion; Estimate for 2022 Indicates Further Increase to $886  Billion . NSF 24-317. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf24317/ .

Report Author

Gary Anderson Senior Economic Advisor NCSES Tel: (703) 292-9092 E-mail: [email protected]

National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences National Science Foundation 2415 Eisenhower Avenue, Suite W14200 Alexandria, VA 22314 Tel: (703) 292-8780 FIRS: (800) 877-8339 TDD: (800) 281-8749 E-mail: [email protected]

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Studies have found that every dollar invested in R&D generates nearly two dollars in return. While the rate will vary, R&D is an important driver of economic growth. To seize this potential, governments need reliable and precise data. In response, the UIS produces a wide range of indicators on the human and financial resources invested in R&D for countries at all stages of development.

To produce these data, we conduct an annual survey that involves countries and regional partners, such as Eurostat , OECD and RICYT . We also work closely with the African Science, Technology and Innovation Indicators ( ASTII ) Initiative of the African Union.

By working closely with these partners and national statistical offices, we can align and harmonise the surveys and methodological frameworks, such as the Frascati Manua l, used at the global, regional and national levels to ensure that resulting data can be compared across countries. This is essential to gain a global perspective on science and technology.

We also provide training and methodological resources to help countries develop their own national surveys on R&D and improve the quality and use of their data.

Guide to Conducting an R&D Survey: For Countries Starting to Measure Research and Experimental Development

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The 9 technology readiness levels of the dod.

From early concept to an application of a technology in it's final form, the technology readiness level (TRL) is a helpful knowledge-based standard and shorthand for evaluating the maturity of a technology or invention.

The science and technology community employed by the Department of Defense uses the abbreviation TRL in reference to “technology readiness level.” It’s a helpful knowledge-based standard and shorthand for evaluating the maturity of a technology or invention.

One is the lowest level of technology readiness and nine is the highest.

It’s important to understand this because the technology readiness level scale might be referenced when small businesses are  licensing technology from the DoD.

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Here’s the definition of each TRL so you can familiarize yourself with the scale.

Technology Readiness Level 1: Basic principles observed and reported

Lowest level of technology readiness. Scientific research begins to be translated into applied research and development (R&D). Examples might include paper studies of a technology’s basic properties. Supporting information includes published research that identifies the principles that underlie this technology, references to who, where, and when.

Technology Readiness Level 2: Technology concept and/or application formulated

Invention begins. Once basic principles are observed, practical applications can be invented. Applications are speculative, and there may be no proof or detailed analysis to support the assumptions. Examples are limited to analytic studies. Supporting information includes publications or other references that outline the application being considered and that provide analysis to support the concept.

Technology Readiness Level 3: Analytical and experimental critical function and/or characteristic proof of concept

Active R&D is initiated. This includes analytical studies and laboratory studies to physically validate the analytical predictions of separate elements of the technology. Examples include components that are not yet integrated or representative. Supporting information includes the results of laboratory tests performed to measure parameters of interest and comparison to analytical predictions for critical subsystems. References to who, where, and when these tests and comparisons were performed.

Technology Readiness Level 4: Component and/or breadboard validation in a laboratory environment

Basic technological components are integrated to establish that they will work together. This is relatively “low fidelity” compared with the eventual system. Examples include integration of “ad hoc” hardware in the laboratory. Supporting information includes system concepts that have been considered and results from testing laboratory scale breadboard(s). And references to who did this work and when. Documentation provides an estimate of how breadboard hardware and test results differ from the expected system goals.

Technology Readiness Level 5: Component and/or breadboard validation in a relevant environment

Fidelity of breadboard technology increases significantly. The basic technological components are integrated with reasonably realistic supporting elements so they can be tested in a simulated environment. Examples include “high-fidelity” laboratory integration of components. Supporting information includes results from testing laboratory breadboard system are integrated with other supporting elements in a simulated operational environment. How does the “relevant environment” differ from the expected operational environment? How do the test results compare with expectations? What problems, if any, were encountered? Was the breadboard system refined to more nearly match the expected system goals?

Technology Readiness Level 6: System/subsystem model or prototype demonstration in a relevant environment

Representative model or prototype system, which is well beyond that of TRL 5, is tested in a relevant environment. Represents a major step up in a technology’s demonstrated readiness. Examples include testing a prototype in a high-fidelity laboratory environment or in a simulated operational environment. Supporting information includes results from laboratory testing of a prototype system that is near the desired configuration in terms of performance, weight, and volume. How did the test environment differ from the operational environment? Who performed the tests? How did the test compare with expectations? What problems, if any, were encountered? What are/were the plans, options, or actions to resolve problems before moving to the next level?

Technology Readiness Level 7: System prototype demonstration in an operational environment

Prototype near or at planned operational system. Represents a major step up from TRL 6 by requiring demonstration of an actual system prototype in an operational environment (e.g., in an aircraft, in a vehicle, or in space). Supporting information includes results from testing a prototype system in an operational environment. Who performed the tests? How did the test compare with expectations? What problems, if any, were encountered? What are/were the plans, options, or actions to resolve problems before moving to the next level?

Technology Readiness Level 8: Actual system completed and qualified through test and demonstration

Technology has been proven to work in its final form and under expected conditions. In almost all cases, this TRL represents the end of true system development. Examples include developmental test and evaluation (DT&E) of the system in its intended weapon system to determine if it meets design specifications.Supporting information includes results of testing the system in its final configuration under the expected range of environmental conditions in which it will be expected to operate. Assessment of whether it will meet its operational requirements. What problems, if any, were encountered? What are/were the plans, options, or actions to resolve problems before finalizing the design?

Technology Readiness Level 9: Actual system proven through successful mission operations

Actual application of the technology in its final form and under mission conditions, such as those encountered in operational test and evaluation (OT&E). Examples include using the system under operational mission conditions. Supporting information includes OT&E reports.

Source: Technology Readiness Assessment Guidance, prepared by the Assistant Secretary of Defense for Research and Engineering

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Research Associate Level 1, 2, or 3

• Anchorage, Alaska, United States
• Staff Full-time
• UAA College of Health

The Institute for Circumpolar Health Studies (www.uaa.alaska.edu/ichs) at the University of Alaska-Anchorage has an opening for a research associate to be involved in research at the intersection of climate change and human health in Alaska under the supervision and mentorship of Dr. Micah Hahn. They will work independently and as a part of a team undertaking all components of the research cycle including grant development, research study design, IRB applications, data collection protocols, data collection, management, and analysis, development of research publications, and presentation of research results. Projects topics are varied and include studies at the intersection of climate and health, e.g. epidemiology research on physical and mental health impacts of wildfires, extreme heat, wild food consumption and food security, water and sanitation in rural Alaska, and creating tools for climate adaptation planning. Most projects require teamwork within our research team as well as collaboration with other researchers and community partners.

Applicants with experience in one or more of the following areas are strongly encouraged: • Experience cleaning, organizing, processing, linking, and analyzing large datasets in R, Python, or similar programming language • Methodological experience in epidemiologic modeling and ability to utilize statistical programming languages to investigate environmental health research questions

Minimum Qualifications:

• Research Professional 1 – Bachelor’s degree in public health, epidemiology, biostatistics, environmental health, or related field and six months research experience. • Research Professional 2 – Bachelor’s degree in public health, epidemiology, biostatistics, environmental health, or related field and one year research experience, or an equivalent combination of training and experience. • Research Professional 3 – Master’s degree and two years research experience in public health, epidemiology, biostatistics, environmental health, or related field, or an equivalent combination of training and experience.

Position Details:

This is a full-time, non-exempt/exempt (dependent on grade hired), term-funded, staff position complete with both a competitive salary and  full employee benefits package . UA provides a generous compensation package that includes retirement options, annual leave, 12 paid holidays per year, tuition waivers for employees and family members, and affordable medical, dental and vision care coverage. New hires will be placed on the  UA Staff Salary Schedule , Level 1 Grade 78, Level 2 Grade 79, Level 3, Grade 80 based on education and experience.

Applications will be reviewed on a rolling basis until a successful candidate is identified. 

To Apply: Applications should be submitted through our Careers site and must include a) cover letter that describes your qualifications and research experience, professional goals, and specific interest in this position; b) Curriculum vitae; and c) Names and contact information for three references. The position will remain open until filled. The anticipated start date is Fall 2024.

*To be eligible for this position, applicants must be legally authorized to work in the United States without restriction.  Applicants who now or may in the future require visa sponsorship to work in the United States are not eligible.

This position is a term-funded position and is reviewed annually for contract renewal at the University's discretion.

The University of Alaska (UA) is responsible for providing reasonable accommodations to individuals with disabilities throughout the applicant screening process. If you need assistance in completing this application or during any phase of the interview process, please contact UA Human Resources by phone at 907-450-8200.

UA is an affirmative action/equal opportunity employer, educational institution and provider and prohibits illegal discrimination against any individual:  www.alaska.edu/nondiscrimination .

The successful applicant is required to complete a background check. Any offer of employment is contingent on the background check.

Your application for employment with the University of Alaska is subject to public disclosure under the Alaska Public Records Act.

If you have any questions regarding this position, please contact Micah Hahn at  [email protected].

* Each university within the University of Alaska system publishes an Annual Security and Fire Safety Report which contains information regarding campus safety and security including topics such as: campus law enforcement authority; crime reporting policies; campus alerts (Timely Warnings and Emergency Notifications); fire safety policies and procedures; programs to prevent dating violence, domestic violence, sexual assault and stalking; the procedures the University will follow when one of these crimes is reported; and other matters of importance related to security on campus. The report also contains information about crime statistics for the three most recent calendar years concerning reported crimes that occurred on campus; in On-Campus Student Housing Facilities; in Noncampus buildings or property owned or controlled by the University or a recognized student organization; and on public property within, or immediately adjacent to and accessible from, the campus.  The report also contains fire statistics for any fires occurring in an On-Campus Student Housing Facility during the three most recent calendar years.  

Access to the reports is available at:

UAA : (Addresses Anchorage campus, Aviation Technology Complex, JBER - Elmendorf Extension, JBER - Richardson Extension, Kenai Peninsula College - Kachemak Bay campus, Kenai Peninsula College - Kenai River campus, Kodiak College, Kodiak High School Extension, Matanuska-Susitna College, Prince William Sound College, and Prince William Sound College - Cordova Extension) Online:  https://www.uaa.alaska.edu/students/safety . Request a paper copy in person: UAA Police Department Office at Room 114 of Eugene Short Hall on the Anchorage campus / UAA Dean of Students Office at Room 122 of Rasmuson Hall on the Anchorage campus. Request a paper copy by mail: 907-786-1120 or  [email protected]  / 907-786-1214 or  [email protected] . 

UAF:  (Addresses Fairbanks Campus, Bristol Bay Campus, Chukchi Campus, Community and Technical College, Kasitsna Bay Campus, Seward Marine Center, Tok Campus, Kuskokwim Campus, and Northwest Campus) Online:  https://www.uaf.edu/orca/files/ASFSR.pdf . Request a paper copy in person: UAF Office of Rights, Compliance and Accountability on the 3rd Floor of Constitution Hall. Request a paper copy by mail: 907-474-7300 or  [email protected] .

UAS:  (Addresses the Juneau Auke Bay Campus, Juneau Technical Education Center, Sitka Campus, & Ketchikan Campus) Online:  https://uas.alaska.edu/equity-and-compliance/docs/clery/UAS_ASFSR.pdf . Request a paper copy in person: Hendrickson Building, Suite 202 on the Juneau campus. Request a paper copy by mail: 907-796-6371 or emailing  [email protected] .

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What the data says about Americans’ views of climate change

A recent report from the United Nations’ Intergovernmental Panel on Climate Change has underscored the need for international action to avoid increasingly severe climate impacts in the years to come. Steps outlined in the report, and by climate experts, include major reductions in greenhouse gas emissions from sectors such as energy production and transportation.

But how do Americans feel about climate change, and what steps do they think the United States should take to address it? Here are eight charts that illustrate Americans’ views on the issue, based on recent Pew Research Center surveys.

Pew Research Center published this collection of survey findings as part of its ongoing work to understand attitudes about climate change and energy issues. The most recent survey was conducted May 30-June 4, 2023, among 10,329 U.S. adults. Earlier findings have been previously published, and methodological information, including the sample sizes and field dates, can be found by following the links in the text.

Everyone who took part in the June 2023 survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

A majority of Americans support prioritizing the development of renewable energy sources. Two-thirds of U.S. adults say the country should prioritize developing renewable energy sources, such as wind and solar, over expanding the production of oil, coal and natural gas, according to a survey conducted in June 2023.

In a previous Center survey conducted in 2022, nearly the same share of Americans (69%) favored the U.S. taking steps to become carbon neutral by 2050 , a goal outlined by President Joe Biden at the outset of his administration. Carbon neutrality means releasing no more carbon dioxide into the atmosphere than is removed.

Nine-in-ten Democrats and Democratic-leaning independents say the U.S. should prioritize developing alternative energy sources to address America’s energy supply. Among Republicans and Republican leaners, 42% support developing alternative energy sources, while 58% say the country should prioritize expanding exploration and production of oil, coal and natural gas.

There are important differences by age within the GOP. Two-thirds of Republicans under age 30 (67%) prioritize the development of alternative energy sources. By contrast, 75% of Republicans ages 65 and older prioritize expanding the production of oil, coal and natural gas.

Americans are reluctant to phase out fossil fuels altogether, but younger adults are more open to it. Overall, about three-in-ten adults (31%) say the U.S. should completely phase out oil, coal and natural gas. More than twice as many (68%) say the country should use a mix of energy sources, including fossil fuels and renewables.

While the public is generally reluctant to phase out fossil fuels altogether, younger adults are more supportive of this idea. Among Americans ages 18 to 29, 48% say the U.S. should exclusively use renewables, compared with 52% who say the U.S. should use a mix of energy sources, including fossil fuels.

There are age differences within both political parties on this question. Among Democrats and Democratic leaners, 58% of those ages 18 to 29 favor phasing out fossil fuels entirely, compared with 42% of Democrats 65 and older. Republicans of all age groups back continuing to use a mix of energy sources, including oil, coal and natural gas. However, about three-in-ten (29%) Republicans ages 18 to 29 say the U.S. should phase out fossil fuels altogether, compared with fewer than one-in-ten Republicans 50 and older.

There are multiple potential routes to carbon neutrality in the U.S. All involve major reductions to carbon emissions in sectors such as energy and transportation by increasing the use of things like wind and solar power and electric vehicles. There are also ways to potentially remove carbon from the atmosphere and store it, such as capturing it directly from the air or using trees and algae to facilitate carbon sequestration.

The public supports the federal government incentivizing wind and solar energy production. In many sectors, including energy and transportation, federal incentives and regulations significantly influence investment and development.

Two-thirds of Americans think the federal government should encourage domestic production of wind and solar power. Just 7% say the government should discourage this, while 26% think it should neither encourage nor discourage it.

Views are more mixed on how the federal government should approach other activities that would reduce carbon emissions. On balance, more Americans think the government should encourage than discourage the use of electric vehicles and nuclear power production, though sizable shares say it should not exert an influence either way.

When it comes to oil and gas drilling, Americans’ views are also closely divided: 34% think the government should encourage drilling, while 30% say it should discourage this and 35% say it should do neither. Coal mining is the one activity included in the survey where public sentiment is negative on balance: More say the federal government should discourage than encourage coal mining (39% vs. 21%), while 39% say it should do neither.

Americans see room for multiple actors – including corporations and the federal government – to do more to address the impacts of climate change. Two-thirds of adults say large businesses and corporations are doing too little to reduce the effects of climate change. Far fewer say they are doing about the right amount (21%) or too much (10%).

Majorities also say their state elected officials (58%) and the energy industry (55%) are doing too little to address climate change, according to a March 2023 survey.

In a separate Center survey conducted in June 2023, a similar share of Americans (56%) said the federal government should do more to reduce the effects of global climate change.

When it comes to their own efforts, about half of Americans (51%) think they are doing about the right amount as an individual to help reduce the effects of climate change, according to the March 2023 survey. However, about four-in-ten (43%) say they are doing too little.

Democrats and Republicans have grown further apart over the last decade in their assessments of the threat posed by climate change. Overall, a majority of U.S. adults (54%) describe climate change as a major threat to the country’s well-being. This share is down slightly from 2020 but remains higher than in the early 2010s.

Nearly eight-in-ten Democrats (78%) describe climate change as a major threat to the country’s well-being, up from about six-in-ten (58%) a decade ago. By contrast, about one-in-four Republicans (23%) consider climate change a major threat, a share that’s almost identical to 10 years ago.

Concern over climate change has also risen internationally, as shown by separate Pew Research Center polling across 19 countries in 2022. People in many advanced economies express higher levels of concern than Americans . For instance, 81% of French adults and 73% of Germans describe climate change as a major threat.

Climate change is a lower priority for Americans than other national issues. While a majority of adults view climate change as a major threat, it is a lower priority than issues such as strengthening the economy and reducing health care costs.

Overall, 37% of Americans say addressing climate change should be a top priority for the president and Congress in 2023, and another 34% say it’s an important but lower priority. This ranks climate change 17th out of 21 national issues included in a Center survey from January.

As with views of the threat that climate change poses, there’s a striking contrast between how Republicans and Democrats prioritize the issue. For Democrats, it falls in the top half of priority issues, and 59% call it a top priority. By comparison, among Republicans, it ranks second to last, and just 13% describe it as a top priority.

Our analyses have found that partisan gaps on climate change are often widest on questions – such as this one – that measure the salience or importance of the issue. The gaps are more modest when it comes to some specific climate policies. For example, majorities of Republicans and Democrats alike say they would favor a proposal to provide a tax credit to businesses for developing technologies for carbon capture and storage.

Perceptions of local climate impacts vary by Americans’ political affiliation and whether they believe that climate change is a serious problem. A majority of Americans (61%) say that global climate change is affecting their local community either a great deal or some. About four-in-ten (39%) see little or no impact in their own community.

The perception that the effects of climate change are happening close to home is one factor that could drive public concern and calls for action on the issue. But perceptions are tied more strongly to people’s beliefs about climate change – and their partisan affiliation – than to local conditions.

For example, Americans living in the Pacific region – California, Washington, Oregon, Hawaii and Alaska – are more likely than those in other areas of the country to say that climate change is having a great deal of impact locally. But only Democrats in the Pacific region are more likely to say they are seeing effects of climate change where they live. Republicans in this region are no more likely than Republicans in other areas to say that climate change is affecting their local community.

Our previous surveys show that nearly all Democrats believe climate change is at least a somewhat serious problem, and a large majority believe that humans play a role in it. Republicans are much less likely to hold these beliefs, but views within the GOP do vary significantly by age and ideology. Younger Republicans and those who describe their views as moderate or liberal are much more likely than older and more conservative Republicans to describe climate change as at least a somewhat serious problem and to say human activity plays a role.

Democrats are also more likely than Republicans to report experiencing extreme weather events in their area over the past year – such as intense storms and floods, long periods of hot weather or droughts – and to see these events as connected with climate change.

About three-quarters of Americans support U.S. participation in international efforts to reduce the effects of climate change. Americans offer broad support for international engagement on climate change: 74% say they support U.S. participation in international efforts to reduce the effects of climate change.

Still, there’s little consensus on how current U.S. efforts stack up against those of other large economies. About one-in-three Americans (36%) think the U.S. is doing more than other large economies to reduce the effects of global climate change, while 30% say the U.S. is doing less than other large economies and 32% think it is doing about as much as others. The U.S. is the second-largest carbon dioxide emitter , contributing about 13.5% of the global total.

When asked what they think the right balance of responsibility is, a majority of Americans (56%) say the U.S. should do about as much as other large economies to reduce the effects of climate change, while 27% think it should do more than others.

A previous Center survey found that while Americans favor international cooperation on climate change in general terms, their support has its limits. In January 2022 , 59% of Americans said that the U.S. does not have a responsibility to provide financial assistance to developing countries to help them build renewable energy sources.

In recent years, the UN conference on climate change has grappled with how wealthier nations should assist developing countries in dealing with climate change. The most recent convening in fall 2022, known as COP27, established a “loss and damage” fund for vulnerable countries impacted by climate change.

Note: This is an update of a post originally published April 22, 2022. Here are the questions used for this analysis , along with responses, and its methodology .

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Alec Tyson is an associate director of research at Pew Research Center

Cary Funk is director of science and society research at Pew Research Center

Brian Kennedy is a senior researcher focusing on science and society research at Pew Research Center

How Republicans view climate change and energy issues

How americans view future harms from climate change in their community and around the u.s., americans continue to have doubts about climate scientists’ understanding of climate change, growing share of americans favor more nuclear power, why some americans do not see urgency on climate change, most popular.

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• 1 College of Management, Sichuan Agricultural University, Chengdu, China
• 2 Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu, China
• 3 Faculty of Economics and Business Administration, Yibin University, Yibin, China
• 4 State Capital Infrastructure Office, Sichuan Agricultural University, Chengdu, China

Increasing demands for livestock products have stimulated rapid increases in the number of livestock and the scale of farming, thus increasing pressure on resources and the environment. Coordinating the development of livestock production (LP) with residential consumption (RC), resources and the environmental carrying capacity (RECC) is important to ensure sustainable development. In this study, the entropy weight method and the improved-coupling coordination degree (CCD) model were used to identify the spatial–temporal coordination development characteristics of livestock production-residential consumption-resource and environmental carrying capacity (LRRE). Furthermore, the spatial autocorrelation model (SAM) and gray model (GM) were used to analyze the spatial aggregation characteristics and future development trends of the CCDs of the LRRE in China. The findings show that the CCDs of the LRRE values in 31 Chinese provinces increased from 2005 to 2020, but no provinces reached a high coordination level. Specifically, the coordinated development level of LRRE is relatively high in the central, eastern and northeastern regions and relatively low in the western region. The spatial autocorrelation analysis confirmed that the high-high (H-H) aggregation areas were mainly distributed in the northeastern, eastern, and central regions, while low-high (L-H) agglomeration was distributed in the western region. This phenomenon is mainly attributed to the continuous expansion of the scale of livestock production in western China. Regions with relatively developed economies have more funds to invest in environmental protection projects. Using GM method, we find that the CCDs of LRRE in 31 provinces in China will increase from 2021 to 2030, and all provinces will reach the basic coordination level. However, most of the western regions will barely reach the basic coordination level. This result indicates that the low level of LRRE development in western China may be difficult to change in the short term. The level of coordinated LRRE development in the relatively developed eastern region is increasing. The spatial layout of China’s livestock industry should be appropriately adjusted, its expansion rate in the western region should be decreased, and the ecological environment of the areas bordering the western and central regions should be improved. These findings have practical implications for other large livestock production countries. Promoting the coordinated development of LRRE is also an important condition for agricultural transformation in developing countries, especially for improving the environment in key areas of the livestock industry.

1 Introduction

Since its reform and opening up, China’s development has led to a significant increase in the demand for livestock products ( He et al., 2018 ; Zhao et al., 2021 ). Currently, China has become the largest producer of livestock products worldwide ( Bai et al., 2018 ; Zhang C. et al., 2019 ). In 2020, China’s meat, egg, and milk production were 899 Mt., 341 Mt., and 368 Mt., respectively ( NBS, 2022 ); worldwide, pork accounted for 37.95% of production, and beef and chicken accounted for more than 10%, respectively ( USDA, 2022 ). However, the expanding scale of livestock farming has placed tremendous pressure on resources and the environment ( Zheng et al., 2019 ). Livestock production releases a large amount of greenhouse gases (GHGs) and uses limited resources, such as land ( Post et al., 2020 ), which affects soil and water safety and poses certain hazards to human health ( Rosa et al., 2020 ). In China alone, compared to their numbers in 2000, the number of pigs in 2022 increased by 36.22 million, theoretically increasing nitrogen and phosphorus discharges by 39.84 and 5.98 10 4 t, respectively ( Zhou et al., 2024 ), placing enormous pressure on river and soil ecosystems. Waste emissions from ruminants in China have been estimated to cost $32.2 billion in damage to ecosystems ( Du et al., 2018 ). Residents living near farms for a long time have also been frequently exposed to respiratory and cardiopulmonary diseases ( Gerbecks et al., 2020 ). Coordinating the relationships among livestock breeders, resources, the environment and residents has become the key to the development of the current breeding industry.

The development of animal husbandry should be compatible with the resource environment and residents’ consumption; an uncoordinated development relationship may lead to an unstable food supply and ecological disaster. Agriculture is a complex process that results from interactions between humans and nature and is intensely affected by resources, society and market price ( Hatfield et al., 2020 ; Seguin et al., 2021 ). Livestock production needs to occur in spaces with substantial regional resources and environmental carrying capacity (RECC), as exceeding the RECC may permanently damage the local ecological structure ( Zhang Y. et al., 2022 ). For example, livestock production is dependent on land and crops to absorb manure, and if resources are insufficient, the ammonia, nitrogen and phosphorus produced during livestock production can severely pollute the air, rivers and soil. With the development of the economy, the rising demand for livestock products will further stimulate growth in the livestock industry ( Sun et al., 2021 ), which will also impose greater environmental pressure ( Qian et al., 2022 ). Population size and residential consumption preferences drive expansion in the livestock sector. If these factors are ignored, the development of the livestock sector may fall short of realistic development goals. For example, provinces with large populations do not have sufficient meat production capacity, which may limit the food supply capacity of these areas. However, although the resource environment and residential consumption play key roles in the development of livestock systems, this relationship is not yet clear; however, an understanding of it is crucial for the layout of China’s livestock industry. Therefore, focusing on in-depth analyses of the coupling coordination degree (CCD) of livestock production (LP), residential consumption (RC), and the RECC (LRRE) is important for evaluating the effect of agricultural policy and promoting the coordinated development capacity of the livestock industry.

Currently, much research considers the impacts of natural disasters on socioeconomic factors ( Ekwueme, 2022 ; Mann and Gupta, 2022 ) and food security ( Jabal et al., 2022 ) or the impacts of extreme disasters on sustainable business development ( Habib and Mourad, 2023 ). While these studies can help readers understand climate change in depth, they neglect to consider livestock factors. Some scholars are currently focusing on the environmental pressures brought about by agricultural production, especially the environmental damage caused by the livestock industry. For example, several scholars have measured theoretical livestock manure emissions in different regions ( Wang et al., 2021 ) and water, soil, and atmospheric pollution caused by livestock production ( Han et al., 2023 ). Nevertheless, few scholars have studied the development of the livestock industry from a resource-environment perspective ( Han et al., 2023 ). Other relevant studies often focus on environmental pressure (carbon emission) or a certain method of agricultural land resource utilization, but pay less attention to coupling coordination ( Jia et al., 2023 ; Liu et al., 2023 ). Several scholars have begun to establish a coupling coordination degree model (CCDM) that analyzes the development-oriented relationships between different systems such as fishery systems and environmental quality ( Peng et al., 2021 ); RECC ( Chen et al., 2022 ; Zhang et al., 2022a ); agricultural green development and food security ( Zhang et al., 2022b ; Zhang and Li, 2022 ); urbanization and the ecological environment ( Ariken et al., 2021 ; Gao et al., 2021 ; Yang et al., 2022 ); tourism and the ecological environment ( Zhang and Li, 2021 ; Zhang et al., 2023 ); and water resource use efficiency and economic development ( Dai et al., 2022 ; Zhang and Li, 2022 ). However, few studies have systematically investigated the coupling coordination relationship of LRRE to analyze the effects of environmental policies and improve ecological development. The United Nation Framework Convention on Climate Change (UNFCCC) proposes improving the capacity for coordinated development between production systems and ecosystems to better respond to climate change ( Liu F. et al., 2022 ). The growing consumer demand for meat has stimulated further expansion of livestock production, greatly increasing pressure on resources and the environment ( Liang et al., 2023 ). Countries impacted by this growth need an approach for achieving the coordinated development of the LRRE system. Therefore, this paper provides an effective reference for other large livestock-producing countries worldwide by analyzing the temporal and spatial changes and future trends in the coordinated development of LRRE in China.

To explore the development relationship of China’s LRRE system, in this study, a coupled relationship evaluation framework for the LRRE system is constructed and an improved-CCDM is used to analyze the CCDs of the LRRE in China from 2005 to 2020. Furthermore, the spatial autocorrelation model (SAM) and the gray model (GM) are used to analyze the spatial aggregation characteristics and future development trends of the CCDs of LRRE in China. The contributions of this paper are as follows: (1) The resource environment and residential consumption play crucial roles in the development of the livestock industry. However, the coupling coordination development relationship between these variables has not yet been demanded; this information is critical for determining the layout of livestock production. Therefore, a framework for assessing the sustainable development of the livestock industry is constructed from the perspectives of LP, RC and RECC, thus providing practical assistance in guaranteeing food security, adjusting the layout of the livestock industry and alleviating environmental pressure. (2) In the construction of indicators, in contrast to existing studies, in this study, we consider GHGs to be an undesired output of livestock production systems; furthermore, we collected and utilized data on the meat consumption of residents in each region of China, thus creating a comprehensive LRRE indicator system. (3) Methodologically, we use an improved-CCDM to calculate the CCD of the RECC, increasing the accuracy of the results, and the SAM and GM to analyze the spatial aggregation characteristics and future trends of the CCD for each Chinese province from 2021 to 2030. This analysis strategy is helpful for researchers and policymakers because it more clearly explains the spatial distribution characteristics and evolutionary trends of the CCD of the LRRE in each province in China and provides a realistic basis for agricultural green development policy formulation.

2 Index selection, methods, and data sources

2.1 index selection, 2.1.1 livestock production systems.

LP systems contain social, economic, and ecological elements ( Zhao et al., 2021 ). The social element refers mainly to the quantity of food (meat, egg, and milk) produced by livestock. The economic element consists of the total output value of the livestock industry and the value of livestock production per unit of farmland area. The ecological elements include livestock manure, nutrient supplies from livestock manure, and GHG emissions from livestock farming processes. Livestock manure, manure nutrient supply, and GHG emissions are calculated as follows:

1. Calculation of livestock manure

The study selected pigs, sows, poultry, beef cattle, dairy cows, sheep, horses, donkeys, and mules as subjects. According to Li et al. (2022) , rearing quantity was based on the slaughter of pigs and poultry; stockpiles were used to calculate the quantities of the other animals. In addition, due to the influences of rearing methods, climatic environment and other factors, the manure emission factors of various livestock in different areas are differed We referred to Zhou et al. (2014) to set the livestock manure emission factors of each livestock type ( Table A.1 in Supplementary material A ). The specific calculation formula (1) is as follows:

Q m a n u r e is the quantity of manure excreted by livestock; P i is the number of livestock i slaughtered or stockpiled. T i is the rearing time of livestock i , and μ i is the manure emission factor of livestock i .

1. Calculation of nutrient supply from livestock manure as follows equation (2) :

S m a n u r e is the livestock manure nutrient supply; φ i is the nitrogen or phosphorus emissions of livestock i ; and θ is the nutrient retention rate. We referred to the recommended values given in the Technical Guide to Livestock Manure Land Carrying Capacity Measurement, which indicated that the nutrient retention rates of nitrogen and phosphorus are 65% ( MARD, 2018 ).

1. Calculation of greenhouse gas emissions from livestock farming as follows equation (3) :

GHGs are the GHG emissions from livestock farming, P R u m i n a n t s represents the stockpile of ruminants, ε i is the enteric fermentation methane emission factor, Q m a n u r e is the quantity of livestock manure, σ i is the livestock manure methane emission factor, and τ i is the livestock manure nitrous oxide emission factors. We referred to Guo et al. (2017) to define enteric fermentation methane emission factors, livestock manure methane emission factors, and livestock manure nitrous oxide emission factors ( Table A.2 in Supplementary material A ).

2.1.2 Resource and environmental carrying capacity

The RECC subsystem consists of resource carrying capacity and environmental carrying capacity systems ( Zhang F. et al., 2019 ). In this paper, referring to Chen et al. (2022) and Zhao et al. (2022) , we selected agricultural land area, crop sown area, irrigated area, regional crop nutrient demand, regional crop manure nutrient demand, fertilizer use, total feed production, rural electricity consumption, total water resources, and road mileage for the evaluation of the resource subsystem. To evaluate the environmental subsystem, we selected the forest coverage rate, wetland area, pollution control investment, annual average PM 2.5 concentration, and green area.

The calculations of regional crop nutrient demand and nutrient requirements for crops from livestock manure were based on the Technical Guide to the Measurement of Land Carrying Capacity of Livestock and Poultry Manure ( MARD, 2018 ). The calculation process is as follows equation (4) :

1. Calculation of regional crop nutrient demand:

Q N u t r i e n t s is the regional crop nutrient requirement, P i is the yield of crop i , and γ i is the crop i nutrient demand coefficient ( Table A.3 in Supplementary material A ).

1. Calculation of the manure nutrient requirements of crops from livestock manure as follows equation (5) :

where Q M a n u r e f e r t i l i z e r is the nutrient requirement of crops from livestock manure; α i represents the share of chemical fertilizer nutrients in the total nutrient requirements of crops; β i is the proportion of livestock manure nutrients to the total nutrient requirements of crops; and δ i is the utilization rate of livestock manure nutrients. For convenience, we referred to the recommended values given in the Technical Guide to Livestock Manure Land Carrying Capacity Measurement ( MARD, 2018 ), where β i is 50%, the utilization rate is 30% for nitrogen and 35% for phosphorus ( Zheng et al., 2019 ), and α i is 45%.

2.1.3 Residential consumption

The residential consumption system consists of the livestock product consumption level and consumption potential. The main evaluation indicators of consumption potential were GDP, GDP per capita , urbanization level, per capita income, population size and Engel’s coefficient ( Cao et al., 2019 ; Roux et al., 2021 ). The consumption level included per capita meat, egg, and milk consumption demand, as well as the total meat, egg, and milk consumption of residents ( Fan and Fang, 2020 ). Table 1 shows the LRRE indicator system.

Table 1 . Indicator system of LRRE.

2.2 Methods

2.2.1 improved-ccdm.

The entropy weight method can be used to comprehensively evaluate several dimensions; it is often used in comprehensive evaluation research because of its scientific basis and simplicity. In this study, the entropy weight method was adopted to calculate the total score of each subsystem; the specific calculation methods used are described in Sahoo et al. (2017) and Zhang C. et al. (2019) . The total score of each subsystem was calculated. Then, the improved-CCDM was used to calculate the CCD of the LRRE. The CCDM is mainly used to evaluate the strength and correlation between the interactions of systems and has been widely used in several fields ( Cheung and Ma, 2011 ; Tan et al., 2022 ). The calculation is as follows equation (6) ( Ariken et al., 2021 ):

where C represents the coupling degree between LRRE, and f ( L P ) , g ( R C ) , and y ( R E C C ) are the evaluation indices of LP , RC , and RECC , respectively.

Furthermore, we can calculate the CCD of LRRE by formulas (7 , 8 ):

where D is the CCD for the three systems, 0 ≤ D ≤ 1; when D is closer to 1, the CCD is higher. T is the comprehensive evaluation index for the coordinated development of the LRRE subsystems. α , β , and μ represent the weight values of the LRRE subsystems. We referenced Shen et al. (2018) and Jiang et al. (2022) and used the improved-CCDM to calculate α , β , and μ . The equations are as follows equation (9) :

Li J. et al. (2021) and Peng et al. (2021) were referenced to construct the coupling coordination level ( Table 2 ). In Table 2 , we divide coupling coordination degree into five levels. When the degree of dissonance coordination increases to a high level, the coordination level of the LRRE system increases, and livestock production, residential consumption and resource environment match.

Table 2 . Coupling coordination level of LRRE.

2.2.2 Spatial autocorrelation

Moran’s I is commonly used to measure the spatial aggregation characteristics of and period changes in industry ( Ping et al., 2004 ; Zhou et al., 2014 ). Therefore, we used the global Moran’s I to test the spatial correlation of LRRE with equation (10) :

where y i and y j are the levels of CCD in province i and province j , respectively; y ¯ is the average level of CCD; w i j represents the elements in row i and column j of the spatial weight matrix; n is the number of provinces; and s 2 is the sample variance.

To adequately express the trend of regional differences, we further used the local spatial autocorrelation method to reveal the correlation of local study units in the neighborhood space ( Li Q. et al., 2021 ). The specific expressions are as follows equation (11) :

2.2.3 GM (1.1) prediction model

The GM (1.1) is the traditional prediction model and can develop and utilize explicit and implicit information from modest data to determine the mathematical relationships between factors. Typically, discrete models are used to construct a model for the period-by-period analysis of intercropping. However, discrete models can perform only short-term analysis of the development of an objective system, and cannot adapt to the requirements of long-term analysis, planning and decision-making. Therefore, the GM is more suitable for short-term forecasting analysis ( Jiang et al., 2022 ) and is widely used for prediction studies ( Qian and Wang, 2020 ; Geng et al., 2021 ; Liu H. et al., 2022 ). Therefore, to visualize the dynamic evolution trajectory of the CCD of the LRRE in the future, we adopted this method to forecast the CCD of the LRRE from 2021 to 2030 in 31 provinces in China. The GM (1.1) model is constructed as follows:

First, the original sequence determined by is the following equation (12) :

Second, the whitening differential equation is calculated as follows equation (13) :

where a is the development gray number and u is the endogenous control gray number.

Furthermore, the solution of equation (13) is equation (14) :

2.3 Data sources

The data were obtained from the China Statistical Yearbook, China Rural Statistical Yearbook, China Animal Husbandry and Veterinary Statistics Yearbook, China Provincial Statistical Yearbooks and the website: https://data.cnki.net/ . All the data are time series data from 2005 to 2020. In this study, we referred to Sun et al. (2019) for the division of China’s four regions. 1

3.1 Comprehensive evaluation results of the LS, RC, and RECC

In this study, the entropy method was first used to calculate the comprehensive scores for LS, RC and RECC from 2005 to 2020 as a way to understand the development of each system. The scores for each system are shown in Figure 1 .

Figure 1 . The spatial patterns of RECC, LP, and RC in China from 2005 to 2020. (A–L) stands for the order of the pictures, which is explained in the text.

Resource and environmental carrying capacity (RECC). Figures 1A – D , show that the RECC in China’s provinces increased from 2005 to 2020. This result is similar to the findings of Liao et al. (2020) . However, unlike Li et al. (2022) , who studied the RECC at the national level, our study focused on 31 provinces in mainland China. This approach can be used to analyze the spatial and temporal trends of China’s RECC more effectively. Specifically, in 2005, except for Sichuan and Inner Mongolia, the RECCs of all the provinces in China were relatively low (<0.3). With the rapid development of China’s economy and policy support, the RECC levels of Chinese provinces improved comprehensively; those of Henan, Shandong, Jiangsu, Guangdong, and Heilongjiang exhibited the most obvious increase. Jiangsu and Heilongjiang reached 0.4–0.5 in 2015 and 2020, respectively, and had the highest provincial RECC levels in China. Guangdong, Jiangsu and Shandong are coastal cities and the best economically developed provinces in China. A good economy ensures that these areas have sufficient funds to improve the environment. However, Beijing, Tianjin, Ningxia, and Hainan had the weakest RECCs, with no significant improvement between 2005 and 2020. Ningxia, located in western China, is characterized by arid and semiarid regions, and its severe environment leads to a relatively weak resource carrying capacity. Moreover, although western China’s RECC has improved, it was still at a low level (0.1–0.2). Beijing and Tianjin are economically developed cities in China. However, the high level of urbanization and large population consumes a large amount of resources, which is a massive challenge for RECC. Overall, improvements of in factors such as the scale of agriculture, water resources, infrastructure and pollution treatment capacity have greatly contributed to the RECC. However, some regions are weak due to differences in economic levels, resource reserves, and pollution treatment among provinces ( Liao et al., 2020 ; Tan et al., 2022 ).

Livestock production (LP) ( Figures 1E – H ). From 2005 to 2020, the spatial and temporal characteristics of China’s livestock production changed significantly. In 2005, the regions with higher-intensity livestock production were concentrated in Shandong, Henan, Hebei, Sichuan, and Inner Mongolia, all with livestock production intensities above 0.3. In 2010, livestock production intensity increased significantly in Heilongjiang, Liaoning, and Inner Mongolia, reaching 0.351, 0.353, and 0.457, respectively. In 2020, the intensity of livestock production in the western regions of Yunnan, Sichuan, and Xinjiang, central regions of Anhui, Hubei, and Hunan, and northeastern region of Liaoning further increased, especially in Sichuan and Liaoning, which had livestock production values of 0.430 and 0.442, respectively. These regions have good conditions for livestock development, such as fertile grasslands, agricultural land, and water resources; their excellent resource endowments have led livestock development policy to gradually tilt toward the western and northeastern regions. China’s 14th Five-Year Plan for Agriculture also identifies the western and northeastern regions as the main livestock production areas. By producing high-resolution maps of livestock production in China, Cheng et al. (2023) found that the intensity of livestock production in China is increasing, especially in Northwest China and in rural areas, and while this growth contributes to reducing hunger and poverty, it may increase pressure on cleaner production.

Residential consumption (RC). Figures 1I – L shows the evolution characteristics of the spatial distribution of livestock product consumption by residents in China from 2005 to 2020. In 2005, the highest livestock product consumption levels occurred mainly in Shandong, Jiangsu and Guangdong, within a range of 0.3 to 0.4. In 2010, Shandong had the highest livestock product consumption level (0.402); furthermore, the consumption level in the central and eastern regions, such as Hubei, Hunan, Anhui, Zhejiang, and Fujian, increased to above 0.2. In 2015, the livestock product consumption levels in the eastern and central regions rose to above 0.2, with Beijing, Henan, Hebei, Zhejiang, Shandong, Jiangsu, and Guangdong reaching levels above 0.3. In 2020, the livestock product consumption level in the eastern region further increased, especially in Shandong and Guangdong, which had the highest levels in China. In the western and northeastern regions, only Xinjiang reached a livestock product consumption level of 0.4–0.5. In fact, the eastern region, as China’s pioneer in opening up to the outside world, has enormous economic development advantages ( Zhang Y. et al., 2022 ). Rapid economic development has attracted a larger population and increased residential consumption levels ( Zhang Z. et al., 2022 ). The results of our study also reinforce this social phenomenon.

3.2 Spatial–temporal characteristics of the CCDs of the LRRE in China

As shown in Figure 2 , the CCDs of the LRRE in 31 provinces of China increased from 2005 to 2020; however, only a few provinces reached an intermediate coordination level (0.6–0.8) in 2020, and most provinces exhibited basic coordination (0.4–0.6). This situation indicates that there is still potential for further coordinated development of LRRE in China. Among them, Ningxia, Xinjiang, and Gansu had the highest annual average growth rates, reaching 2.14, 2.37, and 2.46%, respectively. Shandong, Tibet, Sichuan, Shanxi, and Jilin Provinces had average annual growth rates of less than 1%. The CCDs of the LRRE reached an intermediate coordination level in major livestock production provinces such as Shandong, Henan, Hebei, Sichuan, and Heilongjiang. These provinces have relatively excellent resource endowments but still have not reached a high coordination level, primarily because although the RECCs of these regions are constantly improving, the production intensity of livestock products and the consumption level of residents are rising faster. Hainan, Tibet, Qinghai, and Ningxia have always had a low coordination level. All these provinces are situated in the western region; furthermore, Tibet, Qinghai, and Ningxia are characterized by high-quality pastoral areas in China, sparse populations, and poor resource endowments, which lead to relatively low CCDs.

Figure 2 . CCDs of the LRRE in China from 2005 to 2020.

As shown in Figure 3 , the spatial distribution characteristics reveal relatively high CCDs in the central, eastern and northeastern regions and relatively low CCDs in the western region. Specifically, only Hebei and Shandong reached an intermediate coordination level in 2005, and the surrounding areas were mostly between the basic coordination and low coordination level. No province was at the dissonance coordination levels. In 2010, Henan Province increased from a basic coordination level to an intermediate coordination level, and coastal regions essentially reached a basic coordinate level. Jiangsu reached the intermediate coordination level in 2015, as did Heilongjiang, Sichuan, and Guangdong in 2020. From 2005 to 2020, except for Tibet, Qinghai, Ningxia, and Hainan in the western region, all the provinces achieved a basic coordination level. This shows that the development of China’s livestock industry has not been at the expense of the environment in relation to increases in the consumption levels of residents; rather, the relationships among production, consumption, and resources and the environment has continued to shift toward coordinated development ( Fan et al., 2020 ). However, at the same time, the coordinated development level of LRRE in some provinces in China, such as Beijing, Tianjin, Ningxia, Qinghai, and Tibet, remains at a low coordinated level. These areas should pay attention to matching multidimensional systems such as livestock production, resources and the environment, and residential consumption. Any system that develops more slowly than others may negatively impact residents’ welfare and social economy.

Figure 3 . Spatial distribution of the CCDs of the LRRE in China.

3.3 Spatial autocorrelation analysis of the CCDs

Table 3 presents the global spatial autocorrelation results for the CCD of the LRRE. The results showed that the global Moran’s I was positive and significant ( p  < 0.1), indicating that the spatial distribution of the CCDs of the LRRE in China was positively correlated and significantly clustered. Furthermore, Figure 4 shows the local spatial clustering characteristics of the LRRE in 2005, 2010, 2015, and 2020 in 31 Chinese provinces, with a significance level less than 10%. The main aggregation regions were located in the eastern, central, northeastern, and some western regions of China. Specifically, the high-high (H-H) aggregation areas were mainly distributed in the northeastern, eastern, and central regions. In particular, Hunan, Jiangxi, and Fujian changed from low-high (L-H) aggregation to H-H aggregation, indicating that the influence of these three provinces on the surrounding areas became positive. The L-H aggregation regions were mainly concentrated in the junction regions of the central and western provinces, such as Shanxi, Shaanxi, Ningxia, Gansu, Chongqing and Guizhou Provinces, indicating that the CCDs in these provinces were lower than that in the surrounding areas. These regions are on the Loess Plateau and have a relatively harsh ecological environment ( Li J. et al., 2021 ; Li Q. et al., 2021 ), which should be noted by the Chinese government. Moreover, we found that the H-H aggregation in Jilin shifted to L-H aggregation from 2005 to 2010, indicating that the CCD in Jilin decreased compared to that in the surrounding areas. The possible reasons for this phenomenon were that the intensity of livestock production and consumption level in Jilin Province increased; however, the RECC did not simultaneously increase.

Table 3 . Global Moran’s index of CCD in China from 2005 to 2020.

Figure 4 . Local spatial autocorrelation results of the CCDs of the LRRE in China from 2005 to 2020.

3.4 CCD prediction results

Predicting the coordinated development trend of LRRE in different regions of China can provide a valuable reference for policy makers. Figure 5 shows that the CCDs of all 31 Chinese provinces will increase from 2021 to 2030, and all provinces will be at a level of basic coordination. From 2021 to 2030, Gansu, Anhui, Hubei, Hunan, Liaoning, Xinjiang, and Zhejiang will transition from a basic coordination level to an intermediate coordination level. In 2030, Fujian (0.592) and Shanghai (0.597) will reach an intermediate coordination level, and Henan (0.752), Xinjiang (0.766), and Shandong (0.799) will reach a high coordination level. Although Ningxia, Qinghai, Tibet, and Guizhou Provinces will reach a basic coordination level in 2030, the CCD levels in these regions will still be lower (less than 0.5), suggesting more potential for improvement. These prediction results indicate that the low level of coordinated development of LRRE in western China may struggle to change in the short term. The level of coordinated LRRE development in the developed eastern region is increasing. Therefore, in the future, China’s livestock industry should appropriately adjust its spatial layout, reduce the expansion rate of the livestock industry in the western region, and improve the ecological environment in the areas bordering the western and central regions (arid and semiarid regions).

Figure 5 . CCDs prediction results in China from 2021 to 2030.

4 Discussion

A scientific evaluation of the status of sustainable development in the livestock sector has importance for most developing countries, as it relates to stable food supplies and ecological environment improvements. Several researchers have endeavored to explore the environmental catastrophes caused by livestock farming, but additional research is needed to determine how livestock farming can be sustainable. The development of the livestock industry is strongly related to resources, the environment and resident consumption levels, and we have made efforts to analyze the development of these factors via scientific methods and comprehensive data. A modeling assessment of the sustainable development of China’s livestock sector is necessary because China is the largest producer and consumer of meat worldwide, and subject to pollution caused by the livestock industry. Therefore, we selected China as an example to explore the relationships among LP, RC, and the RECC; these relationships can provide useful information for the sustainable development of the livestock industry in China and other major agricultural countries.

The spatial and temporal characteristics of each subsystem of the LRRE in China are obviously different. In particular, there are significant spatial differences between the production and consumption of livestock products. Livestock production is concentrated mainly in the central region and Shandong; however, the intensity of livestock production is increasing in the western and northeastern regions. The consumption of livestock products occurs mainly in economically developed and densely populated areas (eastern and part of central China). This finding is similar to the findings of Yang et al. (2022) , who reported that large quantities of meat are consumed in the central and eastern regions of China, where the population is concentrated, though livestock farming in the western pastoral areas is also expanding due to the rising demand for meat. As the economy grows, the demand for meat will continue to rise, placing more environmental pressure on the areas with concentrated livestock production ( Sun et al., 2021 ). Fortunately, the overall RECC level in China is increasing providing a high level of ecological security for livestock production. However, the Chinese government should also focus on some western regions (Qinghai, Ningxia, and Gansu) where the RECC level is still low and not conducive to the green development of the livestock industry.

Overall, the CCDs of the LRRE in China’s provinces increased from 2005 to 2020, indicating that China’s livestock industry is transforming to green development. However, notably, whether the western region can continue to sustain the continuous growth of consumption demand for livestock products is a key issue. The CCDs of Qinghai, Tibet, and Ningxia have not significantly improved over the past decade. The results of the spatial autocorrelation test and prediction also reveal that the western provinces exhibit obvious L-H aggregation characteristics; Tibet, Gansu, Ningxia and Guizhou will have the lowest CCDs in China in 2030. These findings indicate that the ability of western China to sustain expansions in the scale of the livestock industry is both a present and future concern. In fact, the Chinese government proposed ecological civilization construction as early as 2012; this concept requires improving the ecological environment by adjusting the industrial layout and investment in environmental management ( He et al., 2023 ). Furthermore, the pattern of China’s livestock production has changed. The scale of livestock production in the eastern and southern water network areas has been continuously reduced, and the ecological environment has improved ( Zheng et al., 2021 ). However, despite the decrease in the scale of livestock in the eastern and southern regions, the increase in consumer demand has led to the need to expand livestock production in other regions of China. In addition, western China has a large amount of pasture and land, which is conducive to expanding livestock production ( Klotzbucher, 2009 ). However, this scenario has exacerbated ecological degradation in western China ( Briske et al., 2015 ). Several studies have shown that the soil and pastures in western China have been severely damaged by the continued expansion of grazing ( Dong et al., 2020 ; He et al., 2023 ). In fact, the Chinese government has advocated using the production method of “grazing prohibition, resting grazing and rotational grazing” to reduce the ecological impact of grazing, but this method is not conducive to the livelihoods of herders and does not support the growing consumer demand for meat ( Harris, 2010 ). Currently, the Chinese government urgently needs to re-examine the pattern of livestock production and implement measures to improve the RECC in western China ( Shang et al., 2014 ).

As population size and affability continue to increase, the global demand for meat consumption continues to rise, resulting in more serious environmental problems. Livestock production requires large amounts of water, grassland, and forage crops; meat production is also a major source of GHG emissions. In some developed countries, the demand for meat consumption is high. However, the layout of the livestock industry is coordinated with residents’ dietary habits and resource endowments, so the livestock industry has not caused severe damage to the environment, as in the United States ( Tonsor and Lusk, 2022 ). The sustainable development of the livestock sector is also strongly related to national economic transformation policy. Australia has one of the highest rates of meat consumption worldwide’ ( Ford et al., 2023 ). To avoid the adverse impact of the large-scale livestock industry on the environment, the Australian government requested residents to reduce excessive meat consumption and improve the agricultural production environment, which ultimately reduced the environmental pressure caused by the livestock industry ( Sievert et al., 2022 ). Developing countries are experiencing rapid population and economic growth; thus, the consumption of meat and the scale of animal rearing are expanding, which creates challenges for environmental sustainability ( Alobo Loison and Hillbom, 2020 ; Ronaghi and Ronaghi, 2021 ). Therefore, promoting the coordinated development of LRRE is an essential condition for agricultural transformation in developing countries ( Benson and Mugarura, 2013 ).

5 Conclusion and policy implications

5.1 research conclusion.

With the growth of the economy, residential demand for meat is increasing, which in turn stimulates the expansion of the livestock industry; however, overfarming can harm the environment. Therefore, promoting the coordinated development of LRRE systems has become the key to sustainable development. This study established an analytical framework for the coupling coordination development of the LRRE. The SAM and GM (1.1) were used to analyze the spatial correlation and future trends of the CCD of the LRRE systems. This analysis strategy can help policy makers understand the development pattern of China’s LRRE system while providing effective evidence for optimizing the layout of China’s livestock sector. The main conclusions of this study are as follows.

(1) From 2005 to 2020, the levels of China’s LP, RC, and RECC increased. However, there were great differences in the spatial distributions of LP, RC, and RECC in China. The highest intensities of livestock production were concentrated in Shandong, Henan, Hebei, Inner Mongolia, and Sichuan and then shifted to the central, western and northeastern regions. The consumption of livestock products occurred mainly in the eastern coastal areas of China. In comparison to the other provinces, Jiangsu, Heilongjiang, Shandong, Guangdong, Sichuan, and Inner Mongolia had higher RECCs. (2) The CCDs of the LRRE continued to increase in 31 Chinese provinces from 2005 to 2020, with relatively high levels in the central, eastern and northeastern regions and relatively low levels in the western region. (3) The spatial autocorrelation analysis confirmed that the high-high (H-H) aggregation areas were mainly distributed in the northeastern, eastern, and central regions, while low-high (L-H) agglomeration was distributed in the western region. (4) According to the prediction results, the CCDs of the LRRE in 31 Chinese provinces will increase to different degrees from 2021 to 2030, and all provinces will reach the basic coordination level. However, most of the western regions will barely reach the basic coordination level. This result indicates that the low level of LRRE development in western China may be difficult to change in the short term.

5.2 Policy implications

Based on these conclusions, we propose the following policy recommendations.

(1) The Chinese government needs to consider and address the trend of expanding livestock farming in the western and northeastern regions, as well as the still high-intensity livestock production levels in some southern water network areas, such as Anhui, Hunan, and Hubei. The government should adjust its environmental protection policy to reduce ecological and environmental pressure in southern water network areas. Moreover, because Beijing, Tianjin, Ningxia, and Hainan had the weakest RECCs, these areas should reduce the intensity of their livestock production and increase their areas of forests and vegetation. (2) The consumption of livestock products occurs in China’s coastal areas, so large and standardized farms should be appropriately built in these areas to supply residents with primary meat foods to alleviate ecological pressure in other areas. Sustainably guiding people’s food consumption and encouraging them to eat poultry and eggs instead of beef or mutton will help reduce the consumption of water and food and reduce the risk of soil degradation. The proportion of meat food imports can be appropriately increased to meet the residential demand for meat food consumption. (3) The CCDs of the LRRE are low in the central and western junction regions, such as Shanxi, Shaanxi, Ningxia, Chongqing, and Guizhou. The Chinese government needs to focus on these areas and provide support. In addition, these areas need to develop cleaner production techniques, protect pastures and vegetation, and improve their RECCs. (4) The eastern and central regions have higher overall CCDs and are in the H-H aggregation area. These regions should take full advantage and of their situation while providing technology and capital to the western region to improve the green development capacity of their livestock industry. The western region is the main production area for beef, mutton, and milk in China. According to the 14th Five-Year Plan for China’s livestock development ( MARD, 2021 ), the western region is likely to further expand its farming scale in the future. However, to protect the environment, the green development capacity of its livestock industry needs to be improved in the western region, large-scale biogas engineering and power generation technology needs to be developed in the highland cold region, and a complete resource recycling system needs to be established to cope with the expanding trend of the livestock industry in the future.

6 Study limitations

The analytical results of this study can guide developing countries in the coupling coordination development of LP, RC and RECC, and help with developing effective measures for adjusting the spatial distribution of the livestock industry. However, this study has several limitations. First, due to the limitation of data availability, the evaluation index system of this study lacks relevant livestock production technology, such as information on the mechanization rate of the livestock industry, the level of digitalization, and the education level of workers; thus, the level of livestock development in some regions may be underestimated. In future research, technical evaluation systems for livestock production can be added, along with relevant technical standards. Second, although this study was based on 31 provinces in mainland China, many of China’s provinces have vast land areas, and the distribution of industrial development, population, climate, and resource conditions within each province varies greatly; therefore, examining the development of the LRRE in terms of provincial-level data may lead to rough generalizations. Therefore, in the future, if higher resolution maps or data can be accessed to analyze the development of LRRE, the layout of China’s livestock industry can be optimized. Finally, although this paper used an improved-CCDM, SAD, and GM, the rapid development of technology may permit future research to use a combination of multidisciplinary techniques such as management science and ecology to assess the environmental impacts and optimize the layout of the livestock industry more accurately.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://data.cnki.net/ .

Author contributions

KZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing. JW: Conceptualization, Formal analysis, Funding acquisition, Software, Writing – original draft. HL: Conceptualization, Data curation, Investigation, Methodology, Software, Writing – original draft. ZZ: Methodology, Formal analysis, Writing – original draft. HW: Supervision, Formal analysis, Project administration, Funding acquisition, Writing – review & editing. JL: Funding acquisition, Resources, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Sichuan Soft Science Research Program [grant no. 2022JDR0174] and [grant no. 22RKX0099].

Acknowledgments

The authors also thank the editors and reviewers for their suggestions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2024.1365076/full#supplementary-material

Abbreviations

CCD, Coupling coordination degree; LRRE, livestock production-residential consumption-resource and environment carrying capacity; H-H, high-high; L-H, low-high; LP, livestock production; RC, residential consumption; RECC, resource and environment carrying capacity.

1. ^ Eastern region: Beijing, Tianjin, Hebei, Shandong, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong and Hainan; central region: Shanxi, Henan, Anhui, Jiangxi, Hubei and Hunan; western region: Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Inner Mongolia, Tibet and Xinjiang; and northeastern region: Liaoning, Jilin, and Heilongjiang.

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Keywords: livestock industry, resource and environmental carrying capacity, development relationship, spatial distribution, future trend

Citation: Zhou K, Wu J, Li H, Zhang Z, Wu H and Li J (2024) Analysis of the coupling coordination of livestock production, residential consumption, and resource and environmental carrying capacity in China. Front. Sustain. Food Syst . 8:1365076. doi: 10.3389/fsufs.2024.1365076

Received: 03 January 2024; Accepted: 19 March 2024; Published: 19 April 2024.

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Copyright © 2024 Zhou, Wu, Li, Zhang, Wu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jianqiang Li, [email protected]

† These authors have contribution equally with the first author and can be considered as co-first authors

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Chromosome-level genome assembly and population genomics reveals crucial selection for subgynoecy development in chieh-qua.

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Min Wang, Zhenqiang Cao, Biao Jiang, Kejian Wang, Dasen Xie, Lin Chen, Shaoqi Shi, Songguang Yang, Hongwei Lu, Qingwu Peng, Chromosome-level genome assembly and population genomics reveals crucial selection for subgynoecy development in chieh-qua, Horticulture Research , 2024;, uhae113, https://doi.org/10.1093/hr/uhae113

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Chieh-qua is an important cucurbit crop and well popular in South China and Southeast Asia. Despite its significance, its genetic basis and domestication history are unclear. In this study, we have successfully generated a chromosome-level reference genome assembly for the chieh-qua ‘A36’ using a hybrid assembly strategy that combines PacBio long reads and Illumina short reads. The assembled genome of chieh-qua is approximately 953.3 Mb in size and is organized into 12 chromosomes, with contig N50 of 6.9 Mb and scaffold N50 of 68.2 Mb. Notably, the chieh-qua genome is comparable in size to the wax gourd genome. Through gene prediction analysis, we have identified a total of 24,593 protein-coding genes in the A36 genome. Additionally, approximately 56.6% (539.3 Mb) of the chieh-qua genome consists of repetitive sequences. Comparative genome analysis revealed that chieh-qua and wax gourd are closely related, indicating a close evolutionary relationship between the two species. Furthermore, we did not detect no recent whole genome duplication (WGD) event in chieh-qua. Population genomic analysis, employing 129 chieh-qua accessions and 146 wax gourd accessions, demonstrated that chieh-qua exhibits greater genetic diversity compared to wax gourd. We also employed GWAS method to identify related QTLs associated with subgynoecy, an interested and important trait in chieh-qua. And the MYB59 ( BhiCQ0880026447 ) exhibited relatively high expression levels in the shoot apex of four subgynoecious varieties compared with monoecious varieties. Overall, this research provides insights into the domestication history of chieh-qua and offers valuable genomic resources for further molecular research.

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