Technology innovation and sustainability: challenges and research needs

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  • Volume 23 , pages 1663–1664, ( 2021 )

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Global environmental problems, such as depletion of natural resources, various types of environmental pollution and health risks, climate change, and loss of biodiversity, have become increasingly evident. Societies are more aware of the challenges than ever, and understand more deeply that pursuing sustainability is essential to environmental protection, economic growth, and social stability. Among solution approaches, technology innovation is a key, as it can influence prosperity, consumption pattern, lifestyle, social relation and cultural development. Technology determines, to a great extent, the demand for raw materials and energy, the ways and efficiency of manufacturing, product performance, waste reduction and waste handling, health and safety, transportation and infrastructure, etc., thereby making significant impacts on the economic, environmental, and social dimensions of industrial development. It is more widely recognized that sustainability is a key driver of innovation, and only those companies that make sustainability as a goal will achieve competitive advantage (Nidumolu et al. 2009 ; Kiron et al. 2012 ).

In the U.S., a new wave of technology innovations has arisen, largely due to the national endeavor to advance manufacturing in the thrust areas of national importance. The accelerated innovations entail rapid transfer of new technologies into design and manufacturing of high performance products and services. Although new and emerging technologies have become an engine of change and progress, the net improvement brought to the environment and society could be questionable, if sustainability principles are not fully incorporated into the technology development and application phases. For instance, although introduction of nanomaterials has created new opportunities for high performance applications and novel product introduction, there exist various concerns about negative impacts on health and the environment. Biofuel, as another example, can be converted directly from renewable sources, but its global emergence has led to the debate over the environmental impact, including global warming, due to growing vegetation used for biofuel manufacturing. All these demand thorough examination of economic, environmental and social aspects. Industries are more seriously conducting comprehensive sustainability assessment, and demand more sustainable technologies (Dornfeld 2014 ).

The essential component of industrial sustainability is three-pillar-based balanced development. This requires that technology innovations be shaped to incorporate sustainability principles fully throughout their development and application phases. It is imperative, therefore, to conduct a fundamental study on the sustainability dimensions of technology innovation, and develop systematic methodologies and effective tools for technology inventors, decision makers, and organizations to evaluate and maximize potential sustainability benefits of new and emerging technologies. In this endeavor, sustainability assessment of technology innovation, especially in its early development stage, is critical.

Technology assessment (TA) emerged in the 1970s as a research based policy advising activity. It constitutes a scientific and societal response to problems at the interface between technology and society. In the last decade or so, early engagement in TA occurred mainly in new and emerging products using, for example, nanotechnology and biotechnology (Grunwald 2009 ). Today, TA is considered a designation of approaches and methods for investigating the condition for and consequence of technologies, and for denoting their social evaluation. It is an interactive and communicative process that aims to contribute to the formation of public and political opinion on societal aspects of science and technology. A number of important concepts exist at the uppermost level of TA operationalization, such as participative technology assessment (evaluations participated by scientific experts, societal groups, and political decision makers), constructive technology assessment (constructive involvement of technology development process, aiming to analyze its enculturation by society), leitbild assessment (explanation of the course of technology development ex post rather than by giving indications on how to shape technology), and innovation-orientated technology assessment (analysis of completed and current innovation processes with primary interest in factors that are crucial to successful market penetration). The known methods for conducting TA are basically all derived based on participants’ views, discussions, and group consensus, and applicable to the TA of individual technology rather than a group of them as a whole. However, there is a lack of scientific framework for systematic, integrated assessment of technology innovation in different life cycle stages. More critically, there has been no systematic methods for TA in the triple-bottom-line-based sustainability space; this could lead to the whole spectrum of sustainability performance of technology innovations unclear.

Sustainability assessment (SA) is a very complex appraisal method. It entails not only multidimensional aspects that may be intertwined, but also cultural and value-based elements. There exist numerous types of sustainability indicators for a variety of systems and applications in different fields, and methods for indicator formulation, scaling, normalization, weighting, and aggregation (Singh et al. 2012 ). Studies on assessment information aggregation leads to a creation of composite sustainability performance indices. Sikdar et al. ( 2012 ) stated that it is deemed desirable to consolidate all the usable indicators into one aggregate metric to make performance comparison easier. A main challenge in SA of technology innovation is how to conduct multiple life-cycle-stage based assessment and to compare sustainability performance under different scenarios, especially when the available system information is uncertain, incomplete and imprecise. In almost every phase of sustainability study, data and information uncertainty issues exist. Examples include the data about material or energy utilization, toxic/hazardous waste generation, and market fluctuation, the multifaceted makeup of the inter-entity dynamics, dependencies, and relationships, the prospect of forthcoming environmental policies, and the interrelationship among the triple-bottom-line aspects of sustainability, weighting methods, weights’ values and aggregation methods. In technology innovation, uncertainty could be more severe, as many types of data and information are frequently unavailable and uncertain, and the relevant information from the literature or other sources may not be easily justifiable.

Apparently, an urgent research need is to develop science-driven frameworks for conducting systematic sustainability assessment of emerging technologies in their early development stage and recommending technologies sets after performing multistage sustainability impact evaluation (Huang 2020 ). Such frameworks should be composed of coherent sets of new concepts, propositions, assumptions, principles, and methodologies, as well as tools that could assist researchers, decision makers, and organizations in shaping technology innovations for industrial sustainability. This is certainly a very challenging task, especially when the world experiences major disruptions, such as COVID-19. However, the motivations for achieving industrial sustainable development goals should lead to the development of a new wave of highly sustainable technology innovations in the years to come.

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This work is supported in part by U.S. National Science Foundation (Award No. 2031385 and 1604756).

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Article Contents

Introduction, 1 installed capacity and application of solar energy worldwide, 2 the role of solar energy in sustainable development, 3 the perspective of solar energy, 4 conclusions, conflict of interest statement.

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Solar energy technology and its roles in sustainable development

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Ali O M Maka, Jamal M Alabid, Solar energy technology and its roles in sustainable development, Clean Energy , Volume 6, Issue 3, June 2022, Pages 476–483,

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Solar energy is environmentally friendly technology, a great energy supply and one of the most significant renewable and green energy sources. It plays a substantial role in achieving sustainable development energy solutions. Therefore, the massive amount of solar energy attainable daily makes it a very attractive resource for generating electricity. Both technologies, applications of concentrated solar power or solar photovoltaics, are always under continuous development to fulfil our energy needs. Hence, a large installed capacity of solar energy applications worldwide, in the same context, supports the energy sector and meets the employment market to gain sufficient development. This paper highlights solar energy applications and their role in sustainable development and considers renewable energy’s overall employment potential. Thus, it provides insights and analysis on solar energy sustainability, including environmental and economic development. Furthermore, it has identified the contributions of solar energy applications in sustainable development by providing energy needs, creating jobs opportunities and enhancing environmental protection. Finally, the perspective of solar energy technology is drawn up in the application of the energy sector and affords a vision of future development in this domain.


With reference to the recommendations of the UN, the Climate Change Conference, COP26, was held in Glasgow , UK, in 2021. They reached an agreement through the representatives of the 197 countries, where they concurred to move towards reducing dependency on coal and fossil-fuel sources. Furthermore, the conference stated ‘the various opportunities for governments to prioritize health and equity in the international climate movement and sustainable development agenda’. Also, one of the testaments is the necessity to ‘create energy systems that protect and improve climate and health’ [ 1 , 2 ].

The Paris Climate Accords is a worldwide agreement on climate change signed in 2015, which addressed the mitigation of climate change, adaptation and finance. Consequently, the representatives of 196 countries concurred to decrease their greenhouse gas emissions [ 3 ]. The Paris Agreement is essential for present and future generations to attain a more secure and stable environment. In essence, the Paris Agreement has been about safeguarding people from such an uncertain and progressively dangerous environment and ensuring everyone can have the right to live in a healthy, pollutant-free environment without the negative impacts of climate change [ 3 , 4 ].

In recent decades, there has been an increase in demand for cleaner energy resources. Based on that, decision-makers of all countries have drawn up plans that depend on renewable sources through a long-term strategy. Thus, such plans reduce the reliance of dependence on traditional energy sources and substitute traditional energy sources with alternative energy technology. As a result, the global community is starting to shift towards utilizing sustainable energy sources and reducing dependence on traditional fossil fuels as a source of energy [ 5 , 6 ].

In 2015, the UN adopted the sustainable development goals (SDGs) and recognized them as international legislation, which demands a global effort to end poverty, safeguard the environment and guarantee that by 2030, humanity lives in prosperity and peace. Consequently, progress needs to be balanced among economic, social and environmental sustainability models [ 7 ].

Many national and international regulations have been established to control the gas emissions and pollutants that impact the environment [ 8 ]. However, the negative effects of increased carbon in the atmosphere have grown in the last 10 years. Production and use of fossil fuels emit methane (CH 4 ), carbon dioxide (CO 2 ) and carbon monoxide (CO), which are the most significant contributors to environmental emissions on our planet. Additionally, coal and oil, including gasoline, coal, oil and methane, are commonly used in energy for transport or for generating electricity. Therefore, burning these fossil fuel s is deemed the largest emitter when used for electricity generation, transport, etc. However, these energy resources are considered depleted energy sources being consumed to an unsustainable degree [ 9–11 ].

Energy is an essential need for the existence and growth of human communities. Consequently, the need for energy has increased gradually as human civilization has progressed. Additionally, in the past few decades, the rapid rise of the world’s population and its reliance on technological developments have increased energy demands. Furthermore, green technology sources play an important role in sustainably providing energy supplies, especially in mitigating climate change [ 5 , 6 , 8 ].

Currently, fossil fuels remain dominant and will continue to be the primary source of large-scale energy for the foreseeable future; however, renewable energy should play a vital role in the future of global energy. The global energy system is undergoing a movement towards more sustainable sources of energy [ 12 , 13 ].

Power generation by fossil-fuel resources has peaked, whilst solar energy is predicted to be at the vanguard of energy generation in the near future. Moreover, it is predicted that by 2050, the generation of solar energy will have increased to 48% due to economic and industrial growth [ 13 , 14 ].

In recent years, it has become increasingly obvious that the globe must decrease greenhouse gas emissions by 2050, ideally towards net zero, if we are to fulfil the Paris Agreement’s goal to reduce global temperature increases [ 3 , 4 ]. The net-zero emissions complement the scenario of sustainable development assessment by 2050. According to the agreed scenario of sustainable development, many industrialized economies must achieve net-zero emissions by 2050. However, the net-zero emissions 2050 brought the first detailed International Energy Agency (IEA) modelling of what strategy will be required over the next 10 years to achieve net-zero carbon emissions worldwide by 2050 [ 15–17 ].

The global statistics of greenhouse gas emissions have been identified; in 2019, there was a 1% decrease in CO 2 emissions from the power industry; that figure dropped by 7% in 2020 due to the COVID-19 crisis, thus indicating a drop in coal-fired energy generation that is being squeezed by decreasing energy needs, growth of renewables and the shift away from fossil fuels. As a result, in 2020, the energy industry was expected to generate ~13 Gt CO 2 , representing ~40% of total world energy sector emissions related to CO 2 . The annual electricity generation stepped back to pre-crisis levels by 2021, although due to a changing ‘fuel mix’, the CO 2 emissions in the power sector will grow just a little before remaining roughly steady until 2030 [ 15 ].

Therefore, based on the information mentioned above, the advantages of solar energy technology are a renewable and clean energy source that is plentiful, cheaper costs, less maintenance and environmentally friendly, to name but a few. The significance of this paper is to highlight solar energy applications to ensure sustainable development; thus, it is vital to researchers, engineers and customers alike. The article’s primary aim is to raise public awareness and disseminate the culture of solar energy usage in daily life, since moving forward, it is the best. The scope of this paper is as follows. Section 1 represents a summary of the introduction. Section 2 represents a summary of installed capacity and the application of solar energy worldwide. Section 3 presents the role of solar energy in the sustainable development and employment of renewable energy. Section 4 represents the perspective of solar energy. Finally, Section 5 outlines the conclusions and recommendations for future work.

1.1 Installed capacity of solar energy

The history of solar energy can be traced back to the seventh century when mirrors with solar power were used. In 1893, the photovoltaic (PV) effect was discovered; after many decades, scientists developed this technology for electricity generation [ 18 ]. Based on that, after many years of research and development from scientists worldwide, solar energy technology is classified into two key applications: solar thermal and solar PV.

PV systems convert the Sun’s energy into electricity by utilizing solar panels. These PV devices have quickly become the cheapest option for new electricity generation in numerous world locations due to their ubiquitous deployment. For example, during the period from 2010 to 2018, the cost of generating electricity by solar PV plants decreased by 77%. However, solar PV installed capacity progress expanded 100-fold between 2005 and 2018. Consequently, solar PV has emerged as a key component in the low-carbon sustainable energy system required to provide access to affordable and dependable electricity, assisting in fulfilling the Paris climate agreement and in achieving the 2030 SDG targets [ 19 ].

The installed capacity of solar energy worldwide has been rapidly increased to meet energy demands. The installed capacity of PV technology from 2010 to 2020 increased from 40 334 to 709 674 MW, whereas the installed capacity of concentrated solar power (CSP) applications, which was 1266 MW in 2010, after 10 years had increased to 6479 MW. Therefore, solar PV technology has more deployed installations than CSP applications. So, the stand-alone solar PV and large-scale grid-connected PV plants are widely used worldwide and used in space applications. Fig. 1 represents the installation of solar energy worldwide.

Installation capacity of solar energy worldwide [20].

Installation capacity of solar energy worldwide [ 20 ].

1.2 Application of solar energy

Energy can be obtained directly from the Sun—so-called solar energy. Globally, there has been growth in solar energy applications, as it can be used to generate electricity, desalinate water and generate heat, etc. The taxonomy of applications of solar energy is as follows: (i) PVs and (ii) CSP. Fig. 2 details the taxonomy of solar energy applications.

The taxonomy of solar energy applications.

The taxonomy of solar energy applications.

Solar cells are devices that convert sunlight directly into electricity; typical semiconductor materials are utilized to form a PV solar cell device. These materials’ characteristics are based on atoms with four electrons in their outer orbit or shell. Semiconductor materials are from the periodic table’s group ‘IV’ or a mixture of groups ‘IV’ and ‘II’, the latter known as ‘II–VI’ semiconductors [ 21 ]. Additionally, a periodic table mixture of elements from groups ‘III’ and ‘V’ can create ‘III–V’ materials [ 22 ].

PV devices, sometimes called solar cells, are electronic devices that convert sunlight into electrical power. PVs are also one of the rapidly growing renewable-energy technologies of today. It is therefore anticipated to play a significant role in the long-term world electricity-generating mixture moving forward.

Solar PV systems can be incorporated to supply electricity on a commercial level or installed in smaller clusters for mini-grids or individual usage. Utilizing PV modules to power mini-grids is a great way to offer electricity to those who do not live close to power-transmission lines, especially in developing countries with abundant solar energy resources. In the most recent decade, the cost of producing PV modules has dropped drastically, giving them not only accessibility but sometimes making them the least expensive energy form. PV arrays have a 30-year lifetime and come in various shades based on the type of material utilized in their production.

The most typical method for solar PV desalination technology that is used for desalinating sea or salty water is electrodialysis (ED). Therefore, solar PV modules are directly connected to the desalination process. This technique employs the direct-current electricity to remove salt from the sea or salty water.

The technology of PV–thermal (PV–T) comprises conventional solar PV modules coupled with a thermal collector mounted on the rear side of the PV module to pre-heat domestic hot water. Accordingly, this enables a larger portion of the incident solar energy on the collector to be converted into beneficial electrical and thermal energy.

A zero-energy building is a building that is designed for zero net energy emissions and emits no carbon dioxide. Building-integrated PV (BIPV) technology is coupled with solar energy sources and devices in buildings that are utilized to supply energy needs. Thus, building-integrated PVs utilizing thermal energy (BIPV/T) incorporate creative technologies such as solar cooling [ 23 ].

A PV water-pumping system is typically used to pump water in rural, isolated and desert areas. The system consists of PV modules to power a water pump to the location of water need. The water-pumping rate depends on many factors such as pumping head, solar intensity, etc.

A PV-powered cathodic protection (CP) system is designed to supply a CP system to control the corrosion of a metal surface. This technique is based on the impressive current acquired from PV solar energy systems and is utilized for burying pipelines, tanks, concrete structures, etc.

Concentrated PV (CPV) technology uses either the refractive or the reflective concentrators to increase sunlight to PV cells [ 24 , 25 ]. High-efficiency solar cells are usually used, consisting of many layers of semiconductor materials that stack on top of each other. This technology has an efficiency of >47%. In addition, the devices produce electricity and the heat can be used for other purposes [ 26 , 27 ].

For CSP systems, the solar rays are concentrated using mirrors in this application. These rays will heat a fluid, resulting in steam used to power a turbine and generate electricity. Large-scale power stations employ CSP to generate electricity. A field of mirrors typically redirect rays to a tall thin tower in a CSP power station. Thus, numerous large flat heliostats (mirrors) are used to track the Sun and concentrate its light onto a receiver in power tower systems, sometimes known as central receivers. The hot fluid could be utilized right away to produce steam or stored for later usage. Another of the great benefits of a CSP power station is that it may be built with molten salts to store heat and generate electricity outside of daylight hours.

Mirrored dishes are used in dish engine systems to focus and concentrate sunlight onto a receiver. The dish assembly tracks the Sun’s movement to capture as much solar energy as possible. The engine includes thin tubes that work outside the four-piston cylinders and it opens into the cylinders containing hydrogen or helium gas. The pistons are driven by the expanding gas. Finally, the pistons drive an electric generator by turning a crankshaft.

A further water-treatment technique, using reverse osmosis, depends on the solar-thermal and using solar concentrated power through the parabolic trough technique. The desalination employs CSP technology that utilizes hybrid integration and thermal storage allows continuous operation and is a cost-effective solution. Solar thermal can be used for domestic purposes such as a dryer. In some countries or societies, the so-called food dehydration is traditionally used to preserve some food materials such as meats, fruits and vegetables.

Sustainable energy development is defined as the development of the energy sector in terms of energy generating, distributing and utilizing that are based on sustainability rules [ 28 ]. Energy systems will significantly impact the environment in both developed and developing countries. Consequently, the global sustainable energy system must optimize efficiency and reduce emissions [ 29 ].

The sustainable development scenario is built based on the economic perspective. It also examines what activities will be required to meet shared long-term climate benefits, clean air and energy access targets. The short-term details are based on the IEA’s sustainable recovery strategy, which aims to promote economies and employment through developing a cleaner and more reliable energy infrastructure [ 15 ]. In addition, sustainable development includes utilizing renewable-energy applications, smart-grid technologies, energy security, and energy pricing, and having a sound energy policy [ 29 ].

The demand-side response can help meet the flexibility requirements in electricity systems by moving demand over time. As a result, the integration of renewable technologies for helping facilitate the peak demand is reduced, system stability is maintained, and total costs and CO 2 emissions are reduced. The demand-side response is currently used mostly in Europe and North America, where it is primarily aimed at huge commercial and industrial electricity customers [ 15 ].

International standards are an essential component of high-quality infrastructure. Establishing legislative convergence, increasing competition and supporting innovation will allow participants to take part in a global world PV market [ 30 ]. Numerous additional countries might benefit from more actively engaging in developing global solar PV standards. The leading countries in solar PV manufacturing and deployment have embraced global standards for PV systems and highly contributed to clean-energy development. Additional assistance and capacity-building to enhance quality infrastructure in developing economies might also help support wider implementation and compliance with international solar PV standards. Thus, support can bring legal requirements and frameworks into consistency and give additional impetus for the trade of secure and high-quality solar PV products [ 19 ].

Continuous trade-led dissemination of solar PV and other renewable technologies will strengthen the national infrastructure. For instance, off-grid solar energy alternatives, such as stand-alone systems and mini-grids, could be easily deployed to assist healthcare facilities in improving their degree of services and powering portable testing sites and vaccination coolers. In addition to helping in the immediate medical crisis, trade-led solar PV adoption could aid in the improving economy from the COVID-19 outbreak, not least by providing jobs in the renewable-energy sector, which are estimated to reach >40 million by 2050 [ 19 ].

The framework for energy sustainability development, by the application of solar energy, is one way to achieve that goal. With the large availability of solar energy resources for PV and CSP energy applications, we can move towards energy sustainability. Fig. 3 illustrates plans for solar energy sustainability.

Framework for solar energy applications in energy sustainability.

Framework for solar energy applications in energy sustainability.

The environmental consideration of such applications, including an aspect of the environmental conditions, operating conditions, etc., have been assessed. It is clean, friendly to the environment and also energy-saving. Moreover, this technology has no removable parts, low maintenance procedures and longevity.

Economic and social development are considered by offering job opportunities to the community and providing cheaper energy options. It can also improve people’s income; in turn, living standards will be enhanced. Therefore, energy is paramount, considered to be the most vital element of human life, society’s progress and economic development.

As efforts are made to increase the energy transition towards sustainable energy systems, it is anticipated that the next decade will see a continued booming of solar energy and all clean-energy technology. Scholars worldwide consider research and innovation to be substantial drivers to enhance the potency of such solar application technology.

2.1 Employment from renewable energy

The employment market has also boomed with the deployment of renewable-energy technology. Renewable-energy technology applications have created >12 million jobs worldwide. The solar PV application came as the pioneer, which created >3 million jobs. At the same time, while the solar thermal applications (solar heating and cooling) created >819 000 jobs, the CSP attained >31 000 jobs [ 20 ].

According to the reports, although top markets such as the USA, the EU and China had the highest investment in renewables jobs, other Asian countries have emerged as players in the solar PV panel manufacturers’ industry [ 31 ].

Solar energy employment has offered more employment than other renewable sources. For example, in the developing countries, there was a growth in employment chances in solar applications that powered ‘micro-enterprises’. Hence, it has been significant in eliminating poverty, which is considered the key goal of sustainable energy development. Therefore, solar energy plays a critical part in fulfilling the sustainability targets for a better plant and environment [ 31 , 32 ]. Fig. 4 illustrates distributions of world renewable-energy employment.

World renewable-energy employment [20].

World renewable-energy employment [ 20 ].

The world distribution of PV jobs is disseminated across the continents as follows. There was 70% employment in PV applications available in Asia, while 10% is available in North America, 10% available in South America and 10% availability in Europe. Table 1 details the top 10 countries that have relevant jobs in Asia, North America, South America and Europe.

List of the top 10 countries that created jobs in solar PV applications [ 19 , 33 ]

Solar energy investments can meet energy targets and environmental protection by reducing carbon emissions while having no detrimental influence on the country’s development [ 32 , 34 ]. In countries located in the ‘Sunbelt’, there is huge potential for solar energy, where there is a year-round abundance of solar global horizontal irradiation. Consequently, these countries, including the Middle East, Australia, North Africa, China, the USA and Southern Africa, to name a few, have a lot of potential for solar energy technology. The average yearly solar intensity is >2800 kWh/m 2 and the average daily solar intensity is >7.5 kWh/m 2 . Fig. 5 illustrates the optimum areas for global solar irradiation.

World global solar irradiation map [35].

World global solar irradiation map [ 35 ].

The distribution of solar radiation and its intensity are two important factors that influence the efficiency of solar PV technology and these two parameters vary among different countries. Therefore, it is essential to realize that some solar energy is wasted since it is not utilized. On the other hand, solar radiation is abundant in several countries, especially in developing ones, which makes it invaluable [ 36 , 37 ].

Worldwide, the PV industry has benefited recently from globalization, which has allowed huge improvements in economies of scale, while vertical integration has created strong value chains: as manufacturers source materials from an increasing number of suppliers, prices have dropped while quality has been maintained. Furthermore, the worldwide incorporated PV solar device market is growing fast, creating opportunities enabling solar energy firms to benefit from significant government help with underwriting, subsides, beneficial trading licences and training of a competent workforce, while the increased rivalry has reinforced the motivation to continue investing in research and development, both public and private [ 19 , 33 ].

The global outbreak of COVID-19 has impacted ‘cross-border supply chains’ and those investors working in the renewable-energy sector. As a result, more diversity of solar PV supply-chain processes may be required in the future to enhance long-term flexibility versus exogenous shocks [ 19 , 33 ].

It is vital to establish a well-functioning quality infrastructure to expand the distribution of solar PV technologies beyond borders and make it easier for new enterprises to enter solar PV value chains. In addition, a strong quality infrastructure system is a significant instrument for assisting local firms in meeting the demands of trade markets. Furthermore, high-quality infrastructure can help reduce associated risks with the worldwide PV project value chain, such as underperforming, inefficient and failing goods, limiting the development, improvement and export of these technologies. Governments worldwide are, at various levels, creating quality infrastructure, including the usage of metrology i.e. the science of measurement and its application, regulations, testing procedures, accreditation, certification and market monitoring [ 33 , 38 ].

The perspective is based on a continuous process of technological advancement and learning. Its speed is determined by its deployment, which varies depending on the scenario [ 39 , 40 ]. The expense trends support policy preferences for low-carbon energy sources, particularly in increased energy-alteration scenarios. Emerging technologies are introduced and implemented as quickly as they ever have been before in energy history [ 15 , 33 ].

The CSP stations have been in use since the early 1980s and are currently found all over the world. The CSP power stations in the USA currently produce >800 MW of electricity yearly, which is sufficient to power ~500 000 houses. New CSP heat-transfer fluids being developed can function at ~1288 o C, which is greater than existing fluids, to improve the efficiency of CSP systems and, as a result, to lower the cost of energy generated using this technology. Thus, as a result, CSP is considered to have a bright future, with the ability to offer large-scale renewable energy that can supplement and soon replace traditional electricity-production technologies [ 41 ]. The DESERTEC project has drawn out the possibility of CSP in the Sahara Desert regions. When completed, this investment project will have the world’s biggest energy-generation capacity through the CSP plant, which aims to transport energy from North Africa to Europe [ 42 , 43 ].

The costs of manufacturing materials for PV devices have recently decreased, which is predicted to compensate for the requirements and increase the globe’s electricity demand [ 44 ]. Solar energy is a renewable, clean and environmentally friendly source of energy. Therefore, solar PV application techniques should be widely utilized. Although PV technology has always been under development for a variety of purposes, the fact that PV solar cells convert the radiant energy from the Sun directly into electrical power means it can be applied in space and in terrestrial applications [ 38 , 45 ].

In one way or another, the whole renewable-energy sector has a benefit over other energy industries. A long-term energy development plan needs an energy source that is inexhaustible, virtually accessible and simple to gather. The Sun rises over the horizon every day around the globe and leaves behind ~108–1018 kWh of energy; consequently, it is more than humanity will ever require to fulfil its desire for electricity [ 46 ].

The technology that converts solar radiation into electricity is well known and utilizes PV cells, which are already in use worldwide. In addition, various solar PV technologies are available today, including hybrid solar cells, inorganic solar cells and organic solar cells. So far, solar PV devices made from silicon have led the solar market; however, these PVs have certain drawbacks, such as expenditure of material, time-consuming production, etc. It is important to mention here the operational challenges of solar energy in that it does not work at night, has less output in cloudy weather and does not work in sandstorm conditions. PV battery storage is widely used to reduce the challenges to gain high reliability. Therefore, attempts have been made to find alternative materials to address these constraints. Currently, this domination is challenged by the evolution of the emerging generation of solar PV devices based on perovskite, organic and organic/inorganic hybrid materials.

This paper highlights the significance of sustainable energy development. Solar energy would help steady energy prices and give numerous social, environmental and economic benefits. This has been indicated by solar energy’s contribution to achieving sustainable development through meeting energy demands, creating jobs and protecting the environment. Hence, a paramount critical component of long-term sustainability should be investigated. Based on the current condition of fossil-fuel resources, which are deemed to be depleting energy sources, finding an innovative technique to deploy clean-energy technology is both essential and expected. Notwithstanding, solar energy has yet to reach maturity in development, especially CSP technology. Also, with growing developments in PV systems, there has been a huge rise in demand for PV technology applications all over the globe. Further work needs to be undertaken to develop energy sustainably and consider other clean energy resources. Moreover, a comprehensive experimental and validation process for such applications is required to develop cleaner energy sources to decarbonize our planet.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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  • Published: 02 April 2020

Environmental technology and a multiple approach of competitiveness

  • Milad Abdelnabi Salem 1 ,
  • Fekri Shawtari 1 ,
  • Hafezali Bin Iqbal Hussain 2 &
  • Mohd Farid Shamsudin 3  

Future Business Journal volume  6 , Article number:  17 ( 2020 ) Cite this article

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Metrics details

This paper investigates the relationships between environmental technology and competitiveness focusing on 224 industrial corporations. To date, there is still a debate regarding the benefits of being green. Previous literature has investigated this relationship mostly in developed countries. Additionally, the majority of these studies do not disaggregate the environmental practices and competitiveness. Less attention has been given to the environmental issues in developing countries. This study aims to fill these gaps by breaking down the environmental technology into two processes and products-focused practices and investigating their effects on the multiple approaches of competitiveness represented by image-, profits-, and satisfaction-related aspects of competitiveness. The study adopts a cross-sectional study using a self-reported questionnaire. The collected data are analysed using structural equation modelling technique based on AMOS methods. The results revealed that only products-focused practices could improve the three dimensions of competitiveness. The processes-focused practices did not contribute to any of the competitiveness aspects. Such results provide new insight for the application of resource-based view theory in green-based developing countries.


Competitiveness reflects the match between the change in the surrounding environment and internal capabilities of corporations [ 48 ]. Companies use their tangible and intangible resources to promote their competitive position [ 97 , 98 ] since they reflect the weaknesses and strengths of the corporations [ 33 ].

Resources, directly and indirectly, support corporations in improving their competitiveness [ 15 , 16 , 48 , 51 , 72 , 79 , 97 ]. It is commonly known that environmental technology could be a source of competitiveness [ 24 , 48 , 63 , 85 , 88 , 92 ]. Environmental technology means using raw materials that have low environmental impact, processing them efficiently, and promoting reutilisation and minimal waste of their final products, thus changing the products and processes of a given production cycle [ 32 ]. The environmental technologies aim to reduce negative impacts of company’s products and services on the environment [ 12 , 44 , 57 , 90 ]. Processes- and products-focused practices are interrelated because engaging in pollution prevention activities requires the consideration of both the products and the processes for manufacturing [ 12 ].

Nevertheless, several studies suggest addressing the two concepts in a separated fashion [ 21 , 24 , 41 , 57 , 58 ]. Klassen and Whybark [ 57 ] stated that activities related to products process include pollution prevention technologies, which require adaptation in both processes- and products-focused practices.

This study relies on resource-based view theory in developing the framework of the study. The researchers have reviewed the related articles to build the hypotheses of the study. The study adopted a cross-sectional survey method, which means the data are collected at one point in time. A survey method is an appropriate tool when the researcher aims to collect data on particular attributes and opinions of a population, and these data are unavailable in secondary sources [ 29 , 96 ]. The following sections discuss the literature review and the methodology of the study.

Literature review

This review focuses on the literature that pertains to the concepts that form the theoretical frame of this paper. By and large, outside of definitions, it elucidates what we currently know about green technology and its relationship with organisational performance according to the resources based view.

Green technology

Green technology refers to the activities related to both products and processes practices. Processes-focused practices refer to activities that intend to install a greater sense of environmental protection in the production processes. This involves measuring things such as using less polluting inputs, redesigning production processes to be less polluting, and recycling products [ 24 , 48 , 85 , 88 , 92 ]. Christmann [ 24 ] noted that such practices could be divided into pollution prevention and innovation of environmental technology. Klassen and Whybark [ 57 ] pointed out that process adaption refers to the fundamental changes to the manufacturing process that reduce any negative impacts on the environment during material acquisition, production, or delivery. Additionally, González-Benito and González-Benito [ 41 ] provided a detailed picture of such practices, classifying processes-focused practices into internal processes-related practices and external processes-related practices.

Internal processes practices consider such things as the installation of emission filters or waste separation, installation preparation systems, acquisition of clean technology, using the renewable resource of energy, and concentration of environmental criteria for production planning, while the external processes practices refer to the activities that consider aspects related to the distribution and supply actions. Such activities can be reflected in the purchase of ecological products, incorporation of environmental performance criteria in supplier selection processes, consolidation of shipments, using cleaner transportation methods, and the establishment of recuperation and recycling systems.

Products-focused practices are related to product aspects aiming to design or develop more environmentally friendly products [ 21 ], which include things such as redesigning product packaging and products to be more environmentally responsible, developing new environmentally responsible products, and advertising the environmental benefits of the production [ 21 , 28 , 57 , 88 ]. González-Benito and González-Benito [ 41 ] classified the products-focused practices into several dimensions, namely using alternative materials that reduce pollution and hazard, reducing resource consumption, designing for disassembly, designing the product in a manner enabling the reusability and recyclability of the product, remanufacturing, and disposal. As a result, products-focused practices intend to make production or the goods less damaging to the environment, which gives extra value to these products or goods. Klassen and Whybark [ 57 ] identify such practices as all investments that significantly modify an existing product’s design to reduce any negative impacts on the environment during any stage of product manufacturing, using, disposing, and reusing.

In general, products- and processes-focused practices can be captured by several indicators that have been widely used by previous literature. These include things such as substituting polluting and hazardous materials/parties with environmentally friendly materials/parties; designing products with a constant focus on reducing resource consumption and waste reduction; designing products that are dismantled, reused, and recycled; preferring green products in purchasing, consolidating the shipments; selecting cleaner transportation methods; using recyclable and reusable packaging/containers in logistics; implementing cleaner processes and technologies; and adopting recuperation and recycling systems (e.g. [ 28 , 41 , 58 , 76 , 87 , 88 ]).

Green technology and competitiveness

Despite the high cost of products- and processes-focused green practices, some historical examples have shown that behaving in an environmentally friendly way could save companies additional costs such as costs related to cleaning up their waste and loss of natural resources. For example, replacing its non-environmentally friendly parts by 3M Pharmaceutical Corporation in California imposed cost of $60,000. However, it removed the corporation annual solvent purchase of $15,000 and the need for $180,000 in emission control equipment. Additionally, it protected the environment since it eliminated around 24 tons of air pollution from the California atmosphere [ 62 ]. Shrivastava [ 90 ] found that focusing on green technological practices could benefit both the surrounding environment and competitiveness.

Similar results are articulated in environmental literature [ 24 , 41 , 54 , 57 , 60 , 76 , 82 , 85 , 90 ]. Porter and Van der Linde [ 73 ] emphasised that environmental innovation can be a means to improve the competitiveness of corporations. Such innovation has been found to have a direct relationship with corporate competitiveness [ 28 ]. Rubashkina et al. [ 80 ] concluded that environmental regulations have a positive influence on the output of innovation activity represented by patents of European manufacturing sectors, which is considered support of Porter’s model. Additionally, Shrivastava [ 90 ] found that adoption of environmental technology can improve the public image of the corporation, which can be considered an aspect of competitiveness. Although Shrivastava considered one aspect of competitiveness represented by the public image to be a result of environmental technology practices, the findings provide indicators that such practices can improve the competitiveness of the corporation. Additionally, eco-design as products-focused practices were found to be significantly related to cost reduction [ 100 ], and the same relationship was observed with reverse logistics [ 35 ]. Also, such a relationship was observed in the study of Lin et al. [ 60 ] that investigated the relationships between market demand, green products, and corporate performance of a sample of 208 Vietnamese motorcycle corporations. The study concluded that there is a positive relationship between green products and corporate performance represented by market position, cost reduction, profits, and reputation. Additionally, Fraj et al. [ 39 ] confirmed that a proactive environmental strategy and innovation favour organisational competitiveness.

Although previous studies came out with similar findings indicating that green practices could improve competitiveness, some argue that engaging in such practices might impose costs and consequently negatively affect the corporation. Sarkis and Cordeiro [ 85 ] investigated the relationships between short-run financial performance represented by return on sales and pollution prevention and the end-of-pipe policies within 482 US firms in 1992. The study found that the end-of-pipe and pollution prevention policies have a negative relationship with financial performance and that pollution prevention had a larger negative relationship with return on sales than did end-of-pipe policy.

Interestingly, González-Benito and González-Benito [ 41 ] found that products- and processes-focused practices had different impacts on different dimensions of corporate performance. While the study found that product design practices had a significant relationship with the market performance (reputation, image, market expectations, and new products), such a relationship seemed to be insignificant with regard to the relationship between processes-focused practices and market performance. However, the study found that both practices do not have significant relationships with other performance measures (e.g. quality, cost, financial performance). Such inconclusiveness in the results of previous studies creates fertile ground for further investigation.

Many regard corporate social/environmental concepts as a Western phenomenon that results from developed institutions and robust systems many of which are hard to find in developing countries [ 4 , 64 ]. Such an understanding would have guided studies to focus on corporate social/environmental concepts and their relationship with the health of an organisation exclusively in developed countries. This disproportionate focus on developing nations means that the same relationship in developing countries has been overlooked. This imbalance is evident in Orlitzky et al. [ 68 ] and Horváthová [ 50 ]. Interestingly, such studies noted that the country location or/and regulations influence how environmental issues relate to corporate performance [ 50 ]. Given this background, our research enriches our understanding of the relationship between corporate social/environmental concepts and organisation in Libya as a developing country.

Moreover, an overview of previous literature has revealed a dearth in research on the subject in developing countries, and specifically in Arab countries such as Libya. For instance, Etzion [ 36 ] stated that very few studies have, until recently, considered how corporate performance requires the consideration of environmental issues in the context of non-developed countries. This oversight necessitates empirical research detailing the relationship between sustainable corporate performance and firm performance for the context of developing countries [ 42 ].

Resource-based view theory

The resource-based view (RBV) theory has been extensively applied in the aim of investigating the relationships between the resources and competitiveness. RBV theory relies on the assumption that performances of companies are varied due to resources heterogeneity across the corporations [ 15 , 16 , 48 , 51 , 97 , 98 , 101 ]. An organisation’s resources constitute its dynamic capabilities and its ability to create, extend, or modify its resources [ [ 9 ], p. 3]. It includes routines that determine an organisation’s accomplish its goals. This depends heavily on tacit knowledge [ 22 , 37 , 56 , 99 ].

The resource-based view theory often overlooks and under-appreciates the importance of the natural environment [ 48 ]. Hart [ 48 ] summarised several capabilities that can be possible sources of competitiveness, namely technology, design, production, procurement, distribution, and services. Consequently, the study assumes that green technology practices could be considered environmental capabilities that capitalise on tacit knowledge that is difficult to observe or replicate [ 22 , 55 ]. Organisations can boost their competitiveness by capitalising on this often overlooked resource [ 22 , 48 , 55 , 78 , 13 ]. Accordingly, the resource-based method promotes the efficient use of resources for improved environmental sustainability and greater competitiveness [ 65 ].

Notwithstanding the importance of RBV, the theory was criticised for its focus on overall performance instead of focusing on the different outputs. For instance, Ray et al. [ 79 ] suggested with the disaggregated dependent variable when testing the RBV. Moreover, there is evidence of the lack of environmental management studies in developing countries [ 4 , 36 , 42 , 64 ]. For instance, Etzion [ 36 ] has stated that only a few studies have investigated the link between green practices and corporations’ performances. Therefore, further empirical research is required in developing countries [ 42 ]. As a response to such calls, the current study aims to determine the influences of green technology practices and competitive aspects of industrial corporations. Consequently, it aims to answer the following question:

To which extent can the green technology explain the competitiveness?

Extensive literature review and in line with RBV theory, the following hypotheses are developed:

Green technology aspects contribute positively to the different aspects of competitiveness.

Products-focused practices contribute positively to the image aspects of competitiveness.

Products-focused practices contribute positively to the profit aspects of competitiveness.

Products-focused practices contribute positively to the satisfaction aspects of competitiveness.

Processes-focused practices contribute positively to the image aspects of competitiveness.

Processes-focused practices contribute positively to the profit aspects of competitiveness.

Processes-focused practices contribute positively to the satisfaction aspects of competitiveness.

The questionnaire development

The items of the questionnaire have been selected from previous environmental management literature to measure the variables as follows: 13 items have been used to represent conventional green practices (e.g. [ 20 , 28 , 41 , 58 , 76 , 87 , 88 ]) and 11 items have been used for the competitiveness (e.g. [ 8 , 28 , 31 , 54 , 61 , 76 , 87 , 88 , 93 , 94 ]).

All questionnaire-based surveys require testing for reliability and validity before conducting the actual survey. Content validity means ensuring the scale can measure what it is supposed to measure [ 46 ]. In other words, the data are considered to be contently validated if experts agree that the instruments of the study include items that can cover all variables [ 14 , 47 , 83 ]. Additionally, Hair et al. [ 46 ] noted that validation refers to referring specialists or experts to review the suitability of the items within the construct. Validity means that the indicators represent the concept accurately while reliability pertains to the consistency between the indicators [ 46 ]. When a questionnaire is valid and reliable, it means that its question is understood clearly by the respondents, and the response options are appropriate [ 96 ].

All items were subjected to reliability and validity test prior to the main data collection. With regard to the content validity of the questionnaire, experts in the same field have checked the questions in the instrument to ensure that they are comprehensive, are relevant, and reflect the phenomena to be measured. Additionally, the researcher conducted two interviews with those in charge of environmental activities in two corporations with characteristics similar to the target population. The respondents’ feedback suggested that the questionnaire is understandable and did not need much time to be completed. The experts equally indicated that since the respondents are familiar with environmental issues, they are likely to be comfortable with the proposed seven-point Likert scale.

Additionally, a sample of 50 environmental managers were randomly choosing to answer the questionnaire for the pilot test. Several studies have recommended that a sample size of 50 could be an adequate for factor analysis [ 30 , 40 , 89 ] and reliability tests [ 47 , 49 , 84 ].

First, we validated the factor structure using exploratory factor analysis. This method is commonly used in environmental literature. For instance, Mardani et al. [ 65 ] researched several prominent databases to determine the frequency of SEM techniques used in studies in the period from 2005 to 2016. Interestingly, they found that around 61% of the published papers have used exploratory factor analysis to validate their data. The items of competitiveness are loaded on three dimensions named image-, satisfaction-, and profits-related aspects with total variance explained value of 55.286. Additionally, the items of green practices variable are loaded on two factors named processes-focused and products-focused explaining the total variance of 62.458.

Second, the reliability test was conducted to insure the existence of the consistency between the indicators [ 46 ]. A Cronbach’s alpha range < 0.6 is poor, moderate between 6 and 7, good when ranging between 7 and 8, very good between 8 and 9, and excellent when equal to greater than 9 [ 46 , 67 ]. If alpha > 0.95, the items should be checked to ensure that they measure different aspects of the concept [ 46 ]. Reliability test resulted in Cronbach’s alpha’s values greater than 0.6, which is considered acceptable as mentioned by Nunnally et al. [ 67 ] and Hair et al. [ 46 ]. Table  1 shows a summary of factor analysis and reliability.

Analysing the main data

After the confirmation of both validity and reliability of the instrument, the actual survey is carried out. The data were collected from a sample of 224 Libyan industrial corporations that represent a response rate of 82%. The target of the study was organisational level as represented by either production manager, environmental management manager, or general manager in small companies [ 95 ]. The following section presents the descriptive statistics of the questionnaire items.

First, Green practices are the activities undertaken by the corporations to make environmental sound regarding their products and manufacturing processes. In general, this variable scored a mean value of 4.14 for all items with a standard deviation of 1.47338. The previous scores indicate that the corporations give moderate importance to these practices.

The mean values of the items ranged from 3.91 to 4.38. The highest value was for preferring green products in purchasing, while the lowest value was for consolidating the shipments. The remaining items were located between these two values as follows: recyclable packaging with a mean value of 3.93, followed by product’s ability to dismantle with a mean value of 3.98, adopting recycling systems with a mean value of 4.07, cleaner transportation methods with a mean value of 4.13, each of reducing resource consumption during the production and product usage stages scored a mean value of 4.18, ecological material in primary packaging with a mean value of 4.19, and finally each of clean processes and technologies and substituting polluting material scored a mean value of 4.32, and finally reducing waste generation during production scored a mean value of 4.34. Table  2 summarises the descriptive statistics of green practices.

Second, competitiveness reflects the degree to which environmental management was beneficial for a number of corporate goals. Items related to the competitiveness have mean values that ranged from 4.10 to 4.92, which indicate that some improvements were gained as results of engaging in environmental activities, especially in aspects related to employees’ retention, sales, and management satisfaction.

Table  3 shows that better recruitment and staff retention recorded the highest mean value of 4.92, followed by achieving higher long-term profits with a mean value of 4.72, increasing sales with a mean value of 4.67, and increasing management satisfaction with a mean value of 4.61, followed by productivity with a mean value of 4.59; both reducing cost and increasing market share have the same mean value of 4.58, followed by each of achieving higher short-term profits and increasing shareholders satisfaction with a mean value of 4.57, improving corporate image with a mean value of 4.53, and finally improving product image with a mean value of 4.10.

In addition to the descriptive part, data were screened for problems in the data that might undermine its validity.

We performed an independent-sample T test to identify the differences between the early and late respondents [ 10 , 17 , 46 ]. The test revealed no significant difference between the two groups. Also, there were no outliers in the data after using Mahalanobis distance, which represent the distance from the case to the centroid of all cases for predictor variables [ 47 , 91 ]. The Harman single factor was also used to identify serious threats in the data due to common method variance [ 45 , 69 , 70 ]. Interestingly, the single-factor model resulted in more than one factor, and the first factor explained 30.497 of the variance, which indicates that common method bias was not a serious threat in this study.

Additionally, correlation matrix shows that there is no evidence of existence of multicollinearity between the variables as all correlation values are less than 0.8 according to the rule of thumb by Hair et al. [ 46 ], who stated that when the correlation between two independent variables is higher than 0.8, it can be an indicator of the existence of multicollinearity, which can deteriorate the results of the analysis. Table  4 shows the correlation results.

Structure equation modelling technique

Structural equation modelling is when multiple variables are studied using statistical methods to determine how they relate to each other [ 47 ]. This technique enables software such as AMOS to be utilised for assessing the confirmatory factor analysis and building the measurement model that is currently allocated before evaluating the structural model (the proposed theoretical framework), which will help in validating the hypothesised model [ 19 , 45 ].

The framework of this research was developed from a review of the literature from which we derived the concepts that framed the research and the analytical tools to process the data, particularly structural equation modelling (SEM) using AMOS. We adopt a reflective model given that our indicators are interchangeable and measure common themes [ 53 , 77 ]. Interchangeable indicators help measure the construct based on several relevant items underlying the domain of the construct [ 25 , 66 ]. It also means that adding or deleting an item will not affect the conceptual domain of the construct [ 53 , 77 ]. This approach is justified as several studies have used it to measure models that comprise few items.

Confirmatory factor analysis (CFA)

CFA was applied for both endogenous and exogenous variables using structural equation modelling (SEM) AMOS 20 technique. The following section discusses the results of confirmatory factor analysis.

First, for the exogenous variables (green practices) Fig.  1 shows that eight items were subject to CFA. It also shows that the P value is significant, which indicates the lack of fit in the exogenous variables. Therefore, Q10 is deleted as it represents the highest modification index item.

figure 1

The hypothesised model of green practices

After deleting Q10, the fit is improved and constructs left with seven items (four items from processes-focused practices and three items from products-focused practices). Figure  2 shows the results of CFA for exogenous variables. It shows that after deleting Q10, all criteria are improved ( P , Chi-square/ df , GFI, TLI, CFI, and RMSEA).

figure 2

CFA of green practices

Second, for the endogenous variables (competitiveness aspects) Fig.  3 shows that 11 items were subject to CFA. It also shows that P value is significant, which indicates the lack of fit in the endogenous variables. Therefore, Q7 was deleted as it represents the highest modification index items.

figure 3

The hypothesised model of competitiveness

After deleting Q7, the fit is improved and constructs left with ten items (four items from profits-related aspects, three items from image-related aspects, and three items for satisfaction-related aspects). Figure  4 shows the results of CFA for endogenous variables. It shows that after deleting Q7, all criteria improved ( P , Chi-square/ df , GFI, TLI, CFI, and RMSEA).

figure 4

CFA of competitiveness

The structural model of the study

After conducting the confirmatory factor analysis for both the exogenous and endogenous variables, the study reached the final structural model as shown in Fig.  5 . The figure shows that five constructs left with 17 items after deleting questions based on their factor loadings and higher modification indexes. Seven items resulted from CFA as probable measurements of green practices within the Libyan industrial sectors and ten items reflect the constructs of the competitiveness. Figure  5 illustrates the final structural model that resulted from AMOS 20.

figure 5

The structural model

The loadings of items range from the lowest 0.47 of profit question 10 to the highest 0.79 of question 3 of the processes-focused practices construct, which reflects that the factor loading of each item is higher than the suggested 0.40 cut-off criteria for SEM loadings [ 47 ].

The reliability test of both environmental technology and competitiveness constructs recorded Cronbach’s alpha values greater than 0.6 for each factor. This is an acceptable range according to Nunnally et al. [ 67 ] and Hair et al. [ 46 ]. Furthermore, correlation matrix recorded no evidence of multicollinearity between the variables as all correlation values are less than 0.8. According to Hair et al. [ 46 ], if the correlation > 0.8, then severe multicollinearity may be present. Table  5 shows the loading of items, correlations, and reliability of the structural model.

These results show that the model is statistically accepted [ 18 , 45 , 47 ]. Additionally, other criteria such as CFI, GFI, TLI, and RMSEA support that the model fits the data very well.

Hair et al. [ 47 ] recommended less than three indicators per construct. Chin [ 23 ] found that structure equation modelling should include a maximum of four items per construct in for acceptable results. More than that, risks produce unacceptable results. With this, we can conclude that the model of this research is acceptable.

The regression weights table (Table  6 ) shows that products-focused practices positively influence the three aspects of competitiveness, which reflects the support of the first three hypotheses (H1.1, H1.2, and H1.3). On the other hand, the table shows that there is not enough evidence to support significant relationships between the processes-focused green practices and the aspects of competitiveness ( P  > 0.05 for all processes-related aspects constructs). Therefore, the last three hypotheses were rejected (H2.1, H2.2, and H2.3). Moreover, it shows that the expected relationships seem to be negative.

The result shows that products-focused practices influence all aspects of organisational competitiveness. Such a result is consistent with previous literature. For instance, Chuang and Huang [ 26 ] found that the competitiveness of Taiwan manufacturing companies was enhanced by incorporating environmental practices. Famiyeh et al. [ 38 ] reached the same conclusion. Additionally, environmental innovation has a positive impact on the competitiveness of Chinese manufacturing enterprises [ 27 ]. Moreover, Lee et al. [ 59 ] found that the competitive advantages of Italian manufacturing SMEs was positively affected by the dimensions of sustainability including the environmental once. Ashton et al. [ 11 ] concluded that clean development mechanisms affect the performance of Malaysian companies positively. Moreover, Junquera and Barba-Sánchez [ 52 ] revealed that Spanish companies experienced cost-based and differentiation-based competitive advantages when they adopted a proactive environmental policy.

On the other hand, processes-focused practices do not have any significant effects on the dimensions of competitiveness. The results indicate any improvements in the processes-focused practices will not lead to any improvements in the competitiveness-related aspects. This result leads to rejecting the last three hypotheses (H2.1, H2.2, and H2.3). These results are consistent with the findings of other studies. For instance, Aboelmaged [ 1 ] found that when Egyptian SMEs integrated technology and environmental regulations, they did not see significant improvements in sustainable manufacturing practices. Additionally, González-Benito and González-Benito [ 41 ] concluded that processes-focused practices do not have significant relationships with performance measures such as quality, cost, financial, and market performance.

According to Poole and Van de Ven [ 74 ], in the case of failed hypotheses, it could be due to temporal differences. For instance, new organisations behave differently to seasoned and established organisations. This reasoning applies to this study as the majority of environmentally related studies focused on established organisations in developed countries which behave differently to organisations in developing countries that do not have the same regulatory framework and corporate environment as those found in developed nations. As seen in this research, this is true for the case of Libya. In summary, the different stages of development could explain why the results of this study differ from those of the majority of the literature.

Exploratory and confirmatory factor analyses were applied to confirm each construct of the model. Doing so resulted in two constructs representing green technology, namely processes- and products-focused practices. Additionally, competitiveness was laid on profits-, satisfaction-, and image-related practices. The study expected that each construct of competitiveness would be explained by each of the green processes- and products-focused practices. This was in line with RBV theory, which assumes that engaging in environmental practices will improve the competitive position of the company [ 48 ], and that only proactive environmental governance is a source of competitiveness, because it was unique to the firm and difficult to obtain by competitors [ 43 ]. However, the results show that only products-focused practices could improve the three dimensions of competitiveness. Additionally, it revealed that processes-focused practices do not contribute to any of the competitiveness aspects. Such results are in line with previous literature [ 41 , 85 ]. It corresponds with the assumption that the profits of the company might be affected by type of environmental innovation rather than the environmental innovation in general [ 81 ]. It also could be due to that products are something that can be seen and evaluated by the customers compared to the processes, which reflect internal intangible resources that cannot be evaluated directly by the customers. Therefore, the consequences of such processes are not valuable unless transformed into tangible outputs. These outputs are represented by products.

The paper contributes to the body of knowledge by stating and testing the potential relationships between each practice of green technology and a multidimensional approach to competitiveness. It contributes to the debate of whether it pays to be green. Additionally, it highlighted the lack of research on environmental issues in developing countries [ 36 , 42 ]. It articulated the competitiveness of Libyan industrial companies as weak [ 2 , 3 , 5 , 6 , 7 , 75 ], and such weakness could be attributed to environmental issues [ 34 , 71 ]. Therefore, it may help to create or improve the awareness of the decision-makers in Libyan industrial corporations towards their environmental actions, and ways to utilise such actions in improving both the surrounding environment and the corporations’ goals.

Despite the contributions of the paper, it has several limitations that should be taken into consideration when referring to this paper. First, the study used a self-reported questionnaire filled in by managers in the study sample. Therefore, survey data might be subject to social desirability bias [ 12 , [ 86 ]]. Second, this study was conducted in Libya, which is considered a developing country, caution should be taken when generalising the results of the study, and the results may be generalised only to a similar environment and stage of development. Third, another limitation of the study is that some items have been deleted from the hypothesised models during the process of CFA, which may affect the validity of the construct. However, reflective models are not disturbed the addition or deletion of an item as it preserves the conceptual integrity of the construct [ 53 , 77 ]. Finally, although 224 industrial corporations can represent an acceptable sample size for this type of study, future studies should increase the sample size to obtain stronger results. This is based on the fact that the sample size can affect the results of a study, and the bigger sample size, the more likely the results are credible and generalisable [ 47 ].


Analysis of moment structures

Structural equation modelling

Confirmatory factor analysis

Goodness-of-fit index

Tucker–Lewis index

Comparative fit index

Root mean square error of approximation

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Authors’ contributions

All the mentioned authors have substantial contributions to the conception of the manuscript, analysis, or interpretation of data for the manuscript. MAS is the corresponding author, and he has built the framework of the study and analysed the main data. FS has major contributions to the study, and the co-author has substantial contribution in screening the data and conducting the pilot study’ test. HBIH has a great contribution in reviewing the data analysis section, and his comments and recommendations have improved the data analysis process and facilitated the interpretation of the data. MFS is the author who summarised the outputs of literature review and reviewed the whole manuscript. All authors have read and approved the manuscript.


We wish to dedicate our acknowledgements and appreciations to all participants in our survey at the industrial companies. Without their cooperation and valuable feedback, it was impossible to complete the study. Additionally, we would like to present our special thanks to the staff at University Utara Malaysia and University Kuala Lumpur for their support.

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Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Silvia liberata ullo.

1 Engineering Department, Università degli Studi del Sannio, 82100 Benevento, Italy

G. R. Sinha

2 Myanmar Institute of Information Technology (MIIT), 05053 Mandalay, Myanmar

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

1. Introduction and Background

Sustainable growth of the whole world depends on several factors such as economy, quality education, agriculture, industries and many others, but environment is one of the factors that plays the most important role. Health and hygiene are key components of the sustainability of mankind and progress of any country, which comes from a clean, pollution free and hazardous free environment. Thus, its monitoring becomes essential so as to ensure that the citizens of any nation can lead a healthy life. Environment monitoring (EM) consists of proper planning and management of disasters, controlling different pollutions and effectively addressing the challenges that arise due to unhealthy external conditions. EM deals with water pollution, air pollution, hazardous radiation, weather changes, earthquake events, etc. The sources of pollution are contributed by several factors, some of which are man-made and others due to natural causes, and the role of EM is precisely to address the challenges so that the environment is protected for a healthy society and world [ 1 ]. With the more recent advances in science and technology, especially artificial intelligence (AI) and machine learning, EM has become a smart environment monitoring (SEM) system, because the technology has enabled EM methods to monitor the factors impacting the environment more precisely, with an optimal control of pollution and other undesirable effects. The design of smart cities is taking the place of old and traditional methods to create and plan urban environments. Smart cities are planned using wireless networks that assist monitoring of vehicular pollution level in the city [ 2 ]. Wireless networks or wireless sensor networks (WSNs) comprise modern sensors which operate on AI based monitoring and controlling methods. Internet of things (IoT) devices are employed in WSNs for effective waste management, vehicle marking, temperature control, and pollution control. Therefore, modern methods of environment monitoring are known as SEM systems, due to use of IoT, AI and wireless sensors [ 3 ]. Assessment of burned areas using multispectral data captured through satellite imaging and remote sensing [ 4 ], mobile health monitoring systems and IoT based environment systems [ 5 ], smart marine environment systems using multimodal sensing networks [ 6 ], and many other SME methods are reported in current literature. When wireless devices are used over a WSN, then certain standards and protocols are important for effective implementation of SEM systems and thus studies are also reported on developing protocols and standards for IoT based SEM systems [ 7 ].

The whole world is working in a comprehensive manner to protect the environment for sustainable agriculture, growth and a healthy society and therefore the main aim of SEM is to address the challenges due to undesirable effects in the environment through smart monitoring so that all key indicators of growth, including the health of society, are well regulated. The environment monitoring methods are implemented for various applications, aiming to serve certain purposes, which may include weather forecasting [ 8 , 9 ], air pollution control [ 10 , 11 , 12 ], water quality control and monitoring [ 1 , 13 , 14 ], and crop damage assessment [ 14 , 15 ], for instance. The objective is to facilitate favorable environment conditions either for agriculture or human beings, or any inhabitants on the earth. The technologies such as IoT and wireless networks have made the monitoring of environment simple and AI controlled. The SEM systems are reported in the literature using different types of smart sensors [ 8 , 16 , 17 , 18 , 19 ], wireless sensor networks (WSNs) [ 11 , 14 , 18 , 20 , 21 , 22 ], and IoT devices [ 1 , 3 , 5 , 8 , 10 , 18 , 23 , 24 ]; these devices, communicating through the networks, have helped the environment monitoring as a smart monitoring system, able to address the challenges in variable conditions.

IoT, WSNs and suitable sensors are the backbone of the SEM systems. The WSNs provide the connectivity of the data, captured by employing sensors and IoT devices, used to record, monitor and control various environmental conditions, such as water quality, temperature, air quality, etc. A smart environment system can be easily understood with the help of an example of a cloud based SEM system, as shown in Figure 1 . The example shown in this figure depicts monitoring of water contamination and its control, by using a cloud based system that connects IoT devices and various suitable sensors. The system can monitor, with the help of IoT devices, if the water is contaminated or clean since all IoT devices have embedded the capability of AI and machine learning. The organization, which is involved in monitoring the water quality of various water sources, has access to the cloud through the data collected from various sensors, for example an aqua sensor, and is subjected to IoT based analysis where the quality check is done.

An external file that holds a picture, illustration, etc.
Object name is sensors-20-03113-g001.jpg

Smart environment monitoring (SEM) system highlighting water contamination and its monitoring using the cloud connecting internet of things (IoTs) and sensors.

One more example of a SEM system, highlighting a general purpose system with extended scope, is shown in Figure 2 ., which shows how the system is addressing various issues related to environment monitoring, such as humidity, temperature, radiation, dust, UV signal etc. The backbone of the system is a WSN that is establishing the actual interface between IoT devices and data captured through various types of smart sensors. This is a perfect example of a “smart city” [ 11 , 25 , 26 ], using a SEM system that ensures healthy environment for its citizen.

An external file that holds a picture, illustration, etc.
Object name is sensors-20-03113-g002.jpg

SEM system addressing various issues in the environment using wireless sensor networks (WSNs) and IoT devices.

By focusing on agriculture, as a relevant issue for the growth of any nation, it is easy to underline how SEM can play a significant role by providing a “smart or green agriculture” [ 14 , 20 , 27 , 28 ], that can deal with major challenges and factors involved in sustainable growth and enhancing productivity within the agriculture sector. One such smart agriculture scenario can be seen in Figure 3 , where a SEM system is actually a smart agriculture monitoring system. In this case, the health of soil, moisture analysis, water contamination level, water quantity level and several other factors are very important in obtaining sustainable productivity in the agriculture sector. We can see in Figure 3 that the smart agriculture monitoring system includes all such factors, controlled and monitored with the help of IoT devices, suitable sensors capturing the agricultural data, then transmitted to the cloud through a WSN.

An external file that holds a picture, illustration, etc.
Object name is sensors-20-03113-g003.jpg

Smart agriculture monitoring system using IoT devices and sensors.

We attempted to study the existing contributions by a critical survey on SEM methods; the literature suggests that the extensive reviews on SEM methods which have discussed significant findings are not found. We could not find much literature that reviews or surveys SEM techniques. A survey on smart agriculture systems [ 29 ], smart home technologies [ 30 ], smart health monitoring systems [ 31 ], environment monitoring [ 32 ], an IoT based ecological system [ 33 ], IoT for marine environment monitoring [ 34 ], and a survey on pollution monitoring system [ 35 ], are a few of the survey and review related articles highlighting different aspects of SEM. The environment is contaminated due to several factors, but water pollution, air pollution, radiation and sound pollution are mainly involved in most of the existing research. This motivates us to bring out an extensive review on SEM that covers all important factors affecting the health of the environment and predominant methods used to mitigate the challenges due to these factors, such as IoT and sensor technologies.

We have briefly discussed in this section the main issues related to environment monitoring, SEM, the role of IoT, AI and WSNs in implementing SEM. The next part of the paper is organized as follows: Section 2 discusses related research and study; Section 3 presents comparative analysis of advances in SEM systems; Section 4 highlights the significance of the study and recommendations).

2. Related Research and Study

The current research suggests that environment monitoring systems are implemented smartly as SEM for various purposes and using different methods. A huge number of contributions on SEM, both based on purposes and types of methods, have been studied and therefore the related research has been discussed in three main subsections, namely the study based on smart agriculture monitoring systems (SAMs), smart water pollution monitoring systems (SWPMs), and smart air quality monitoring systems (SAQMs). In this manuscript the authors have attempted to critically report the major findings and limitations of the current research on SEM. Soil monitoring (SM) [ 14 , 15 , 36 ], ocean environment monitoring (OEM), marine environment monitoring (MEM), air quality monitoring (AQM) [ 10 , 11 , 37 , 38 ], water quality monitoring (WQM) [ 14 , 39 ], and radiation monitoring (RM) [ 1 , 36 ] have been covered, by offering a wide analysis of different application fields of SEM.

While studying the existing literature on SEM methods, especially on advancements in IoT and sensor technologies for SEM systems, we found that an extensive review on this topic has not been much reported. We found some interesting literature on specific areas of research addressing some challenges of environmental factors such as water pollution, air quality, radiation, and smart agriculture. We aimed at bringing out major advances in IoT and sensor technologies used for addressing the challenges in SEM and thus we included some significant research studies and contributions of various sources highlighting specific classic work on SEM methods. The current study on advances in IoT and sensor technologies used for SEM provides insight to the scientists, policymakers, and researchers in developing a framework of appropriate methods for monitoring the environment that faces challenges mainly due to poor air quality, water pollution and radiation. These factors also affect agriculture which is backbone of any developed and developing economy and thus smart agriculture monitoring (SAM) has also been studied in this section.

Table 1 shows major research studies and contributions on the above SM, OEM, MEM, AQM, WQM and RM areas of interest. Soil monitoring methods were reported to have been affected by greenhouse effects. Ocean and marine SEM systems have been implemented using sensors, WSN and IoT and these methods have mainly suffered with cost, coverage and installation issues [ 40 , 41 , 42 ]. Air pollution control and AQM [ 1 , 10 , 11 , 16 , 43 , 44 , 45 ] have been suggested using a mobile sensor network, wireless sensors and IoT devices that operate on AI and machine learning. In a similar manner, we can see in Table 1 that the different types of SEM systems are designed and implemented for various purposes and there is no robust method that can address any of the challenges of environment.

Research studies based on purpose and applications of environment monitoring.

2.1. Study based on Smart Agriculture (SAM)

This section presents studies and research on smart agricultural monitoring (SAM) systems covering the measures for crop monitoring, pest control, fertilizer control etc. The research study summary for a few important works can be seen in Table 2 . Plant growth monitoring [ 54 ] was implemented and named as “gCrop”, using IoT, machine learning and WSN. The work uses a regression model of the 3 rd degree and provides a prediction accuracy of 98% but the computational complexity was high. The analysis of crop quality [ 14 , 46 ] assessment was made using SAR data for monitoring the quality of paddy rice. Support vector machines (SVMs) with back-scattering features were used in this assessment of the rice quality, with a limited sample size. Leaf area and dimension also play an important role in the assessment of various types of crops, as means to determine if the growth is satisfactory or not. One such work was reported in [ 55 ], that was used to measure the leaf area index using SVM as the machine learning technique, with a Gaussian process model [ 56 ] and the accuracy of measurement found as 89% with a limited sample size also in this case. An expert system using AI has been implemented in [ 57 ] using the Naive Bayes [ 58 ] method and machine learning which operates on sensor data captured in agriculture. This work was useful in monitoring the quality of fertilizer, pesticides and the amount of water to be irrigated in the crops. Some other works studied crop quality assessment [ 21 , 59 , 60 ] and [ 61 ] used for monitoring of the soil health, suitable for soya bean crop on the basis of phenological data and unmanned aerial vehicle (UAV) real-time images. There are a few other important studies on various application of SEM systems for different applications, such as smart farming [ 62 ], pest monitoring [ 63 ], and crop area monitoring [ 61 ].

Research on IoT based SEM systems.

SVM: support vector machine; UAV: unmanned aerial vehicle

The environment conditions affect the health of crops and consequently the agriculture growth. Therefore, we aimed at studying the status of research on SEM using IoT, sensors and AI techniques. The factors involved in agriculture such as soil condition, moisture condition, water pollution, air quality, temperature etc. have been taken into consideration while reviewing the advances in SEM methods. The focus is given to studies on water pollution monitoring and air quality monitoring methods also, which are discussed in next sub-sections.

2.2. Study based on Smart Water Pollution Monitoring (SWPM) Systems

Different literature has been studied on smart water pollution monitoring (SWPM) methods and systems using machine learning methods, IoT and wireless sensors. Table 3 depicts a few major contributions in the area of SWPM. Remotely sensed images were analyzed and machine learning was applied for prediction of the pollution level in the lagoon water, useful for agriculture [ 64 ]. This work used ordinary neural network based machine learning and the prediction results were not very satisfactory. Classification of water contamination [ 65 ] has been studied and water was classified as clean or polluted water, using machine learning methods and IoT devices. The paper presented a realtime contamination monitoring system, though the data captured were in a limited area only. The assessment of various pollutants mixed in water has been implemented in [ 66 ] and the pollutants were classified using a DSA-ELM model [ 66 ] with the evaluation of the model itself. AI and neural network based prediction of water quality parameters was studied in [ 67 ], and alkalinity, chloride and sulphate contents were estimated. The work mainly focused on prediction of water quality parameters and values of sulphate or chloride present in the water. Big data analysis and issues in classification of water contamination were discussed for classification of the contamination using SVM in [ 68 ]. Quality assessment of drinking water and its classification into drinkable and non-drinkable water were presented in [ 69 , 70 ], as a real time monitoring system and AI-SVM based classification technique respectively. A video based surveillance of water quality and pollutants, was studied in [ 71 ], and the surveillance helped to stop the man–man sources of pollutants. The work employed IoT tools for video-surveillance and machine learning for classification of water as polluted and clean water. One more work on a drinking water prediction model was suggested in [ 61 ], and a feature based model helped in analysis of drinking water to further predict its quality before usage. In another work, chlorophyll-A concentration in lake water was assessed using different machine learning models [ 72 ], and the work was recommended as method for a realtime lake water cleaning management system.

Research on IoT based smart water pollution monitoring systems (SWPM).

2.3. Study based on Smart Air Quality Monitoring (SAQM)

Research on SAQM methods and systems have also been studied, and Table 4 presents a summary of different SAQM approaches used in recent literature on air quality monitoring systems. Air quality characterization [ 58 ] has been implemented using heterogenous sensors and machine learning methods. The monitoring as well as characterization of water quality was achieved but interoperability issues were reported in this work due to use of heterogenous sensors. Air quality evaluation using fixed as well as mobile nodes of sensors [ 75 ] was implemented, capable to check the air quality in stationary as well as mobile ways. In this latter case, the compatible sensors were deployed as mobile nodes which can work satisfactorily in a moving environment. Data captured through smart sensor nodes were processed and analyzed with the help of machine learning techniques. Another air quality control process was studied using IoT and machine learning techniques in [ 76 ], with a focus on assessment of air pollution, deploying gas sensors which help in capturing air particles and analyzing the pollutants mixed in the air. Sensor networks have been established in moving vehicles for monitoring air quality with the help of machine learning; in [ 77 ], mobile sensor nodes and WSN were deployed. Infrared sensors were deployed to evaluate the air quality, especially analyzing volatile organic compounds (VOCs) in [ 25 ], with the help of machine learning methods. The elements of VOCs were detected and analyzed using spectroscopic observations. There are a few components present in the air that help assessing the quality of the air; one such component, called PM2.5, was predicted in [ 78 ], using extreme machine learning techniques tested upon spatio-temporal data collected in a certain duration of time over a range of distances covered by the sensors. Different forecasting models were suggested in [ 44 ] for quality evaluation of urban air and the components like O3, SO2 and NO2 were determined and a comparison was made for the models used in the work. RFID and a gas sensor based air quality control mechanism were implemented in [ 79 ], to determine the level of pollution in the air by predicting the pollution value; IoT was employed to analyze sensory data captured through gas sensors. RFID was primarily used in this work for detection of pollutants and communicating to WSNs with the help of IoT devices connected across a WSN architecture. An SAQM system has been studied in [ 80 ], using a LoRaWAN (long range WAN) [ 80 , 81 , 82 ], and this work has been very useful for detecting temperature, dust, humidity and carbon dioxide components in the air. An intelligent air quality system was presented for detection of CO2, NOx, temperature and humidity in [ 83 ] using AI and machine learning techniques for developing expert systems for air quality assessment. Furthermore, PM10, PM2.5, SO2, oxides of nitrogen (NOx), O3, lead, CO and benzene components were detected, on the basis of machine learning methods trained by spatio-temporal data, in [ 84 ]. This was extended using deep learning for detection and detailed analysis of O3 components only. Another work employing heterogenous sensors was studied in [ 85 ]. SVM was used for analyzing the sensor data, captured through heterogenous sensors, and air quality was estimated.

Research on SAQM systems using machine learning and IoT.

(VOC: volatile organic compound; LoRaWAN: long range WAN)

3. Discussion, Analysis and Recommendation

This section presents analysis, discussion and a few significant recommendations on the basis of extensive literature review on various SEMs. The SEM systems were studied, covering air quality assessment, water pollution monitoring and agriculture monitoring system, in addition to the sub-subsidiary applications of these three major studies. The recent research contributions were the main focus of the study though a few important research studies, conducted and investigated in last two decades, were also included. The contributions were reported on various SEM methods used for several purposes, mainly air quality assessment [ 1 , 5 , 11 , 12 , 47 , 58 , 76 , 85 , 89 , 90 ]; water pollution monitoring methods [ 1 , 13 , 14 , 39 , 64 , 66 , 71 , 72 , 73 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]; radiation monitoring methods [ 1 , 36 ]; and smart agriculture monitoring systems [ 1 , 14 , 28 , 54 , 60 , 62 , 63 , 98 , 99 , 100 , 101 , 102 ].

The extensive study on SEM methods brings out the following major observations for the discussion:

  • The research on SEM includes various purposes, mainly on SAM, SWPM and SAQM. The study of water pollution, air quality, soil moisture and humidity can help in modeling and design of healthy environment systems that would also help smart agriculture for sustainable growth of the economy.
  • The methods under each of the purposes are divided in terms of sensory data used, machine learning methods used, IoT devices used, and types of sensors involved. The current study made by us mainly focused on impact of existing research on water quality monitoring, air quality assessment, applications of SEM and smart agriculture systems.
  • In most of the SEM methods, especially SAM and SWPM, CNN based deep learning methods are used by the researchers and other deep learning models are not very frequently used.
  • The sensory data vary in most of the applications of SEM and there is no robust data over which a maximum number of methods are operating. The data type and regions of interest are not the same for various research work.
  • The methods have been used for either classification or prediction; for example, water is classified as polluted or clean water; similarly, the water and air quality can be predicted (e.g., level of degradation).

The studies reported for all purposes of SEM systems do not have any common challenges and vary from application to application, but the major challenges observed are as follows:

  • Wherever heterogeneous sensors are used, there is problem of interoperability in the analysis of the data captured through different types of sensors.
  • Sample size is limited in many of the contributions.
  • Noisy data poses a challenge in analysis. Noise is present in the data captured through sensors used for various purposes. The noise may be contributed by several internal and external factors.
  • The machine learning methods which have been employed for training the data and for classification are mostly traditional methods of machine learning, such as SVM, neural network, etc.
  • Fuzzy based methods and deep learning approaches are used in a few research studies and implementations, but the research suffers with either big data issues or huge computational complexity.
  • There is no robust approach of machine learning reported, that can be employed in addressing the challenges of the environment irrespective of the purpose of the monitoring and control, types of data, and types of sensors used.

Research trends were also analyzed to assess the quantum of research carried out in the area of SEM [ 14 , 21 , 31 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ] and Table 5 shows a summary of quantity of research in this case. The study of trends was made by using a publication search in the Science Direct databases in year-wise manner. In this analysis, the duration has been chosen from year 1995 to year 2020. It can be clearly seen in Table 5 that the quantum of research on SEM has been increasing with the time in both the case, namely the research employing IoT and WSN, as well as the research using IoT and machine learning. An interesting fact is an outcome of the table: the research using modern machine learning methods is still lagging behind those which do not use any machine learning. However, if IoT devices are used, and deployed in a WSN, then the role of AI cannot be overlooked.

Quantum of research contributions using IoT and WSN; and IoT and machine learning.

The analysis of research trends are shown in Figure 4 , highlighting the research trends in two main categories, namely SEM using IoT and WSN, and IoT and machine learning, respectively. The trends suggest that the SEM has yet to be implemented and studied widely on machine learning based training and subsequent classification or prediction. The research is reported to have increased every year but more impact of IoT and WSN can be seen in Figure 4 .

An external file that holds a picture, illustration, etc.
Object name is sensors-20-03113-g004.jpg

Trends of SEM methods.

The above discussion and analysis helps us recommending the following for better, robust and smarter environment monitoring systems:

  • A framework of machine learning methods needs to be developed.
  • A robust set of classification, prediction and forecasting models has to be designed that can operate on any data, irrespective of the purpose of using the SEM.
  • Suitable denoising methods are required to be implemented as pre-processing to the SEM major stages, since most of the research has failed using de-noising the data and its appropriate pre-processing.
  • Data deduplication approaches and other methods are needed to deal with big data issues involved in a few significant studies.
  • SEM aims at sustainable development of any nation and the smart agriculture and smart environment play a most important role in achieving the sustainable goals, but in rural areas, in most of the developing and underdeveloped nations, the necessary infrastructure for setting up IoT, WSN and other sensors is still a challenging task. This requires governmental level involvement both at local as well as global perspectives.
  • Interoperability issues in implementing various types of sensors, can be addressed by developing suitable standards and protocols that can make the data compatible for all acquisition and analysis systems.

An attempt was made to include major observations of a few significant review articles on SEM but it was very difficult to report any such extensive review on the SEM in particular. This motivated us to study the most important contributions on research addressing environmental challenges due to main factors. This review helped us to reach to some conclusions and make recommendations for designing a robust SEM systems that can handle all possible challenges using a framework of AI and sensor technologies.

4. Conclusions and Future Scope of Work

This paper has presented an extensive and critical review of research studies on various environment monitoring systems used for different purposes. The analysis and discussion of the review suggested major recommendations. The need of extensive research on deep learning, handling big data and noisy data issues, and a framework of robust classification approaches has been realized. We have focused mainly on water quality, and air quality monitoring as smart agriculture systems that can deal with environmental challenges. The major challenges in implementation of smart sensors, AI and WSN need to be addressed for sustainable growth through SEM. The participation of environmental organizations, regulator bodies and general awareness would strengthen SEM efforts. The poor quality of sensory data can be preprocessed using appropriate filters and signal processing methods to make the data more suitable for all subsequent tasks associated in SEM. The future scope of the work aims at studying other factors of environment such as sound pollution and disasters etc.


Authors acknowledge the help of Samrudhi Mohdiwale, from National Institute of Technology Raipur India, for artwork of the figures included in this paper.

Author Contributions

All authors contributed equally to the work. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with 24start discount code, ii. early work linking technology and the environment with human social organization, iii. impact: considering humanity’s effects on the environment, a. water pollution, b. soil erosion, depletion, and unsustainable agriculture, c. declining biodiversity, d. deforestation, e. global warming, iv. considering the primary human causes of environmental impact, a. technology, b. population, c. affluence, inequality, and consumption, v. looking ahead as society moves through the 21st century, vi. conclusion.

Throughout time, humanity has grappled with questions of how to survive and, in so doing, to meet the needs for basics such as food and shelter. Historically, humankind has used technology to assist in the pursuit of these survival basics. Researchers examining society from a comparative and historical perspective note that as subsistence technology has developed—for example, from the digging stick to the plow to the steam engine—so have there been profound changes in the ways societies themselves are organized (e.g., Lenski 1966; Lenski and Nolan 1984.

With the advance in technology, societies are able to acquire and produce more food and to accumulate surpluses. This leads to a number of profound changes in social and ecological processes, including changes in the numbers of people living in a society, and, more generally, on the planet, and in the patterns of accumulation and distribution of resources among those people. Furthermore, as technology allows for deeper incursions into the earth, the potential for environmental impact increases dramatically (Ponting 1991; McNeill 2000).

Because of the profound implications for the well-being, and perhaps even the long-term survival, of humanity, questions about interactions of social arrangements among human beings, the technologies they produce, and their impacts on the natural environment are vitally important to sociologists. Yet by their very nature, these questions involve a number of aspects, and as such, their study typically has been interdisciplinary. The study of social-technological-environmental interactions, by its very nature, draws on a number of subfields. We now turn to some of the attempts to bring social scientific analysis to these questions.

Some of the early attempts to examine these interrelationships were undertaken by sociologists, but with a heavy influence of other disciplines, most notably biology. These came to be known under a broad rubric of human ecology (e.g., Duncan 1964; Commoner 1971, 1992; Catton 1980, 1994; Catton and Dunlap 1978; Hawley 1981).

Human ecologists developed a framework that came to be known as the POET model, so named because of the acronym formed by the four major variables: population (human social), organization, environment, and technology. While this model served as a useful way to focus discussions about human-environmental interactions, it was not particularly influential in guiding empirical research. One of the chief criticisms spoke to the ecological nature of the model itself, in that it did not specify an outcome and did not make specific predictions (for an in-depth discussion, see Dietz and Rosa 1994).

As sociologists and others came to recognize the limitations of the POET model, it was modified by a number of researchers around several emerging themes. A series of arguments were advanced that a set of models should be specified that could predict environmental impacts, such as deforestation, greenhouse gas emissions, and air and water pollution. As a very general way of conceptualizing the problem, environmental impact was seen as being a function of population, technology, and human consumption levels (which came to be referred to in many of the models as “affluence” because of the high correlation in many societies between levels of wealth and patterns of consumption). They presented the IPAT model, in which (Environmental) Impact = Population * Affluence * Technology (Ehrlich 1968; Commoner 1971, 1992; Ehrlich and Ehrlich 1981, 1990; Dietz and Rosa 1994).

Each of the four terms can be defined in a number of ways, and as such, the IPAT model should be seen as a general framework rather than a specific predictor (Dietz and Rosa 1994). For example, while some of the same social factors that are linked with an environmental impact, such as greenhouse gas emissions, can also be used to predict deforestation, there are important differences as well. While population dynamics are important to consider in predicting environmental impact, specifics about population distributions are often more informative than overall levels of population. Studies, for example, show that rural population growth is much more closely associated with deforestation, while urban population growth is more closely associated with greenhouse gas emissions and levels of resource consumption (Burns, Kick, and Davis 1997, 2003).

Furthermore, the social factors most closely associated with what predicts one greenhouse gas (carbon dioxide) differ in important ways from those predicting another greenhouse gas (methane) (Burns et al. 1994; Burns, Kick, and Davis 1997; Jorgenson 2006). Much of the work has followed in this vein, and in a notable variant, researchers have reformulated the IPAT approach into the STIRPAT model, an acronym for Stochastic Impacts by Regression on Population, Affluence, and Technology (e.g., York, Rosa, and Dietz 2003). While all the specifics of these processes are beyond the purview of this entry, it is nonetheless important to realize that such distinctions as to the scope of precise causes of particular environmental impacts are important for researchers and policymakers to consider. Attention to such detail can often lead to insight about why there are findings that may be characterized as “conflicting” in the popular press. It is thus important to give detailed attention to each of the respective areas of the overall framework, as well as to the overall picture.

In developing countries, approximately 90 percent of human sewage is simply dumped without any attempt at treatment whatsoever (World Resources Institute 1996:71). These discharges often go directly into water; yet even when the dumping is not direct, it often leaches into underground aquifers. Either way, this causes serious pollution problems and the public health risks associated with them. While adequate supplies of safe drinking water become more imperiled worldwide, it is a particularly acute problem in parts of the developing world where population growth is outstripping the local resources. By the most reliable estimates, for instance, by the year 2025, at least a billion people in northern Africa and the Middle East will lack water for basic necessities like drinking and sustaining their crops (Postel 1993).

Runoff of water contaminated by short-sighted farming practices, such as indiscriminate use of synthetic fertilizers, pesticides, and herbicides, as well as from concentrations of livestock animal waste from huge feed lots leads to a number of ecological and health problems, particularly for those living downstream from them (Steingraber 1998; Burns, Kentor, and Jorgenson 2003).

On average, farmland in the United States now has only about two-thirds as much topsoil as it did at the beginning of the nineteenth century (Pimentel et al. 1995). This is directly attributable to poor land management practices, such as raising one crop over large stretches of land (monocrop agriculture) and the extensive use of tractor plows and synthetic fertilizers and pesticides. Typically, this leads to a situation in which soil is either blown away by wind or washed away by rain or by irrigation. Only on about 10 percent of U.S. farmland is soil being replaced as fast as it is being eroded, typically through the slow but rich process of naturally breaking down organic matter (Pimentel et al. 1995).

Historically, societies expanded their food production by increasing the amount of land dedicated to farming and grazing. This worked well as long as there was fertile soil that could be brought under cultivation. However, these increases are necessarily bound by the amount of total land available to a society, and ultimately by the size of the planet. Over time, only less fertile land was available, and people increasingly began to attempt cultivating land that needed something beyond what was available through the natural environment to produce food.

As Rachel Carson noted as early as 1962 in her landmark work The Silent Spring, a number of chemicals the U.S. Army developed under wartime conditions during World War II became generally available to farmers at the end of that war. These included herbicides and pesticides such as DDT, as well as synthetic fertilizers. Already by the 1950s, these had come into widespread use, particularly in developed countries (Brown, Flavin, and Kane 1992).

Since about 1980, the amount of land dedicated to farming has actually been decreasing for the first time in history; this trend is particularly strong in developed countries (Pimentel 1992). While it is true that greater amounts of food can be produced in the short run by the use of monoagriculture, pesticides, herbicides, and synthetic fertilizers, in the longer run, this leads to soil erosion and degradation.

The earth and its subregions are in a delicate ecological balance. Loss of a species leads to a number of problems, not the least of which is that the fragile balance often gets upset, sometimes leading to catastrophic results (Ryan 1992). For example, in the 1920s the people in Kern County, California, decided to eliminate threats to their crops and livestock. They killed every such threat they could find—skunks, coyotes, snakes, foxes, and beavers. For their efforts, they were repaid by being overrun by millions upon millions of mice, in what was (at least to date) the worst rodent infestation in U.S. history (Maize 1977, cited in Eisenberg 1998).

By some estimates, anywhere from 15 to 75 species in tropical rainforests go extinct on an average day (Ehrlich and Ehrlich 1981; Wilson 1990, 1992). Yet many of the “miracle drug” cures come from plants (many of them teetering on the edge of extinction) in those very rainforests (Soejarto and Farnsworth 1989).

The major social causes of deforestation involve population dynamics, the level and growth of economic development, and the structure of international trade (e.g., Rudel 1989; Kick et al. 1996; Lofdahl 2002; Burns, Kick, et al. 2003). However, changing technologies greatly affect all three of these major causes in different ways, meaning that technology affects deforestation indirectly and has done so throughout human history (e.g., Chew 2001; Diamond 2005).

The effects of population are often addressed in the context of urban population growth and rural population growth. For example, rural population growth increases the likelihood that forested regions will be transformed, cut, or burned for use in industrial activities, extractive processes, or agricultural production, and related technological developments only exacerbate the environmental impacts of these activities (Rudel 1989; Burns et al. 1994; Rudel and Roper 1997).

Rudel (1989) and Ehrhardt-Martinez (1998) argue that economic development in less developed countries will increase deforestation by expanding the availability of capital for productive ventures in extractive industries and agriculture (for further discussion, see Marquart- Pyatt 2004). Conversely, Burns, Kick, et al. (2003) find that the least developed countries experience the highest rates of deforestation, followed by middle-developed countries, and highly developed ones sometimes experience attempts at reforestation. This pattern can be attributed, at least in part, to a process of recursive exploitation, in which environmental resources of the least developed countries are acquired at a discount by entrepreneurs and corporate actors from both highly developed and developing nations, while the resources of developing countries accrue primarily to actors in highly developed countries (e.g., Burns, Kick, et al. 2003, 2006; Burns, Kick, and Davis 2006).

In a related vein, higher-consuming countries partially externalize their consumption-based environmental costs to less developed countries, which increases deforestation within the latter (see also Jorgenson and Rice 2005). This externalization largely takes the form of the flow of raw materials and produced commodities from less developed to more developed countries, and technological developments in extractive and productive sectors as well as transport (e.g., shipping) intensify the environmental degradation associated with these asymmetrical international exchanges (e.g., Bunker 1984; Jorgenson and Rice 2005).

The human dimensions of climate change and global warming are perhaps the most widely addressed human-environment relationships in the social sciences and policy venues. There is general consensus in the scientific community that global warming is indeed a reality and that human societies do contribute to the warming of the earth’s atmosphere through activities that lead to the emission of noxious greenhouse gases (National Research Council 1999). Atmospheric greenhouse gases absorb and reradiate infrared energy and heat back to the earth’s surface, which increases water, land, and air temperatures in the biosphere (Christianson 1999).

Two of the most serious greenhouse causing gases emitted into the atmosphere as a by-product of human activity are carbon dioxide and methane. In terms of scale, carbon dioxide accounts for the largest volume of greenhouse gas caused by humans; molecule for molecule, methane is an order of magnitude more effective at absorbing and reradiating infrared energy and heat back to the earth’s surface. The primary human activity contributing to carbon dioxide emissions is the use of fossil fuels. Methane emissions are increased by the refining of fossil fuels as well as through increased cattle production and large-scale agriculture activities, particularly the growing of rice (Jorgenson 2006).

With technological development comes the ability to dig deeper, to go farther into the earth, oceans, and space. While this allows people to produce more food, clothing, shelter, and luxury items, it also makes greater demands on the world’s resources and dramatically increases the accumulation of waste products.

Some analysts argue that the earth is robust enough to cope with waste products and will regenerate itself (e.g., Simon 1983, 1990; Simon and Kahn 1984; for counterarguments, see Ehrlich and Ehrlich 1981, 1990). While almost anything will be broken down and recycled by the natural environment, the question of how long this will take is crucial. For example, a single glass bottle can be broken down, but the process takes about 10,000 years. The use of technology in allowing people to extract resources and then to use them in increasingly exotic combinations has the potential to lead society to the point where the earth will not be able to regenerate itself in time for the human race to live and use technology in the way it does (Ehrlich and Ehrlich 1981).

Technology is most readily available in core societies, but it is also becoming increasingly widespread throughout the world, especially in rapidly developing countries. It is true, however, that if environmental regulation is promulgated at all, it tends to be done primarily in the high-consuming, developed societies. Thus, the developing societies often have a combination of technology with a lack of concomitant regulation. The result is that the developing societies are often places with some of the worst ecological degradation.

The former U.S. Vice President Gore (1993), for example, gives a tragic illustration of some of the social dynamics behind the Aral Sea drying up—a sea that had been the fourth largest landlocked body of water in the world and that had provided a livelihood for thousands of people. A number of factors contributed to this, not the least of which was an irrigation system that had been used to grow cotton in an otherwise desert climate. The cotton was grown originally for economic reasons—it could draw a better price on the world market than virtually anything else that could be grown there, but only in the short run. In the long run, the diversion of water effectively changed the hydrological cycle in that area. Once the hydrological cycle is changed, it is often changed permanently.

In this case, the technology was sophisticated enough to change the natural ecology in a dramatic way. This was done as a short-term response to economic pressures for survival in an increasingly competitive world. There was another component to the problem as well: With the dissolution of the Soviet Union, the Aral Sea was no longer entirely in one state (it was in a part of two contiguous newly created states, Uzbekistan and Kazakhstan). The technological sophistication was not matched by environmental regulation.

If technology can be used to destroy the earth, could it also be used to help repair it? There are a number of beneficial uses of technology, and certainly, technological development can, if done with its environmental consequences in mind, harness some of those benefits. Ecologically sound energy sources, such as solar and wind power, are not currently in a state of development that enables them to compete with fossil fuels under current market conditions. However, with more research, it may well be that these ecologically sound energy sources become generally available.

Some theorists, most notably Julian Simon and his collaborators (e.g., Simon 1983, 1990; Simon and Kahn 1984), hold that technological development will help to alleviate society’s most pressing problems. Most notably, Simon believes that environmental problems will, given enough technology, be overcome. In fact, Simon and his collaborators criticize Malthus ([1798] 1960) and his followers as well. Simon believes that increasing population size will lead to increasing levels of human interaction and, thus, the much greater probability that some of those people will develop critically needed technology.

Consider, too, that the internal combustion automobile, one of the greatest polluters of all time, was originally welcomed as a clean alternative to the pollution caused by horses in city streets. There is an important lesson here. Human actions, including the production and use of technological innovation, almost always have unforeseen or unintended consequences. Nobody develops a technology deliberately to pollute, yet pollution is often a consequence of technology. This is not to say that society should cease trying to develop technologically. Rather, we would do well to approach technology with enough humility to recognize that we cannot always control the outcome and that continually relying on technology to solve environmental problems may be flirting with disaster.

As of the beginning of the third millennium, there are over 6 billion people in the world, and that number is rising rapidly. Most of the very rapid population increases have taken place since the advent of the industrial revolution and the technological advances associated with it. Consider that the world population mark surpassed only 1 billion in about 1850 AD. According to United Nations projections, by the year 2025, that number will be up to 8 billion (United Nations Population Division 1995).

Over two centuries ago, Thomas Malthus ([1798] 1960) noted that the technological progress associated with the beginning of the industrial revolution had a number of consequences for the human race. Malthus thought that with the increasing capacity of production, there would be a tendency for population to increase dramatically. While Malthus saw the ability of society to produce the necessities of life, such as food, clothing, and shelter, as increasing linearly (what he termed “arithmetically”), this would lead people to have many more children, and so the population would increase exponentially (what Malthus termed “geometrically”). The mismatch between the modest growth in the ability to produce resources and the tremendous growth in the size of the population would eventually lead to “overpopulation”; this term that Malthus coined— overpopulation—has been part of human dialogue ever since.

More specifically, Malthus argued that overpopulation and the problems associated with it, such as severe crowding and competition for scarce resources, would eventually lead to serious social problems. Recalling the Apocalypse, or the last book of the Bible, Malthus theorized that overpopulation would lead to its own “four horsemen” of the apocalypse. For Malthus, the four horsemen were war, famine, plague, and pestilence. Malthus has inspired a number of modern-day thinkers, who also see population growth as the central cause of a plethora of social and environmental problems (e.g., Ehrlich 1968; Ehrlich and Ehrlich 1990; Abernethy 1991; Bongaarts 1994; Pimentel et al. 1994; Cohen 1995; see also United Nations Population Fund 1991, 1997, 1999).

While absolute size of the population is crucial, distribution of the population is important as well (Burns et al. 1994; Burns, Kick, and Davis 2006). Dramatic increases in population, particularly in rural areas, often lead to serious environmental degradation in those areas, as people clear previously forested land, for example.

While the world’s population is increasing, and is now over 6 billion people, the greatest population increases are in the least developed countries. Unless resources can be increased (through, e.g., technological advance), the proportion of resources accruing to any given person, especially in the countries that are already the poorest in the world, will likely decrease over time.

While no one knows for sure the precise carrying capacity of the planet, there are a number of trade-offs that eventually must be made. One such trade-off, ultimately, may be a quantity/quality one, in which the planet may support, for example, a population of upward of 10 billion people but at a lifestyle greatly diminished from what is currently the case, especially in developed, mass-consumption-oriented societies (Cohen 1995).

Historically, the more developed a society, the greater the urbanization of that society. A century ago, for example, virtually all the major cities of the world were in developed countries. Over time, however, particularly in the late twentieth and the twenty-first centuries, the rapidly developing countries, such as India and Mexico, have been urbanizing very rapidly. United Nations (1992) projections are that some time in the first half of the twenty-first century, nine of the ten largest cities in the world will be in what world-system theorists would classify as semiperipheral countries.

With urbanization comes the concentration of humanly created waste, which is produced much faster than the time it takes to biodegrade. Hence, a number of environmental problems associated with urbanization will very likely continue to plague the Third World even more in the years to come. However, rural population growth also brings its unique problems. It is often the case that deforestation is precipitated by encroachment into rural areas.

An important idea in ecology is that of carrying capacity of the natural environment. Although it was originally conceptualized in terms of animal and plant species, with some important caveats, it applies to human beings as well (Catton 1980, 1994; Cohen 1995). Carrying capacity of an area refers to the number of members of a species that can live in that area. For animals, the area poses natural limits by virtue of the food and shelter available and in terms of the threats to a species’ livelihood through exposure to disease and competition from predators.

With some important caveats, many of the theories that have been developed to describe nonhuman populations can apply to human populations as well. The use of language and other complex symbol systems makes the human case quite distinct, however. Technology is made possible through those complex symbol systems and the accumulation of knowledge that accompanies them. This, in turn, makes it possible to alter the natural environment profoundly. While it is true that every species has an effect on its environment, human beings have, by far, had the most profound effect of all (Lenski 1966; Schnaiberg and Gould 1994).

Human beings can use technology to extend the carrying capacity of a place temporarily. The use of fossil fuel such as gasoline is a good example. Through techniques such as drilling into the earth and refining the crude oil found there, we are able to use energy that was fixed millennia ago. In so doing, we extend the carrying capacity, but we do so only temporarily. The oil itself takes much longer for nature to produce than for us to use it. Ecologists see the temporary extension of carrying capacity through technology as a prime case of overshoot (Catton 1980). However, it is also a principle of ecology that overshoot tends to be followed by some catastrophe that causes severe hardship and death. This condition is often referred to in the literature with the apocalyptic moniker of “crash”; historically, the greater the overshoot, the greater the severity of the eventual crash (Catton 1980; see also Diamond 2005).

As we have seen, population growth is related to environmental impact in a number of complex ways (Burns et al. 1998). Ultimately, every individual requires a certain amount of energy to survive. However, the level of affluence must be very carefully considered as well. There is a great deal of inequality, both within and among countries, in terms of the level of affluence.

In 1960, the richest 20 percent of the world’s population had an income about 30 times that of the world’s poorest 20 percent. Within one generation—by 1990—that proportion had doubled to 60—the richest fifth of the world’s population had incomes 60 times that of the poorest fifth (United Nations Development Programme 1994). With increasing affluence comes the increasing impact, or size of the “ecological footprint,” a person or a society makes (Jorgenson 2003; York, Rosa, and Dietz 2003).

Closely associated with the question of overall affluence is the question of how unevenly that affluence is distributed. In fact, one of the greatest critics of Thomas Malthus, and his ideas on overpopulation, was Karl Marx. Marx believed that the central human problem was distribution of resources, with a few people living in luxury, while many lived in poor, and increasingly desperate, conditions. While Marx had little to say about the effect of this on the environment (for an attempt to link Marx’s work with environmental concerns, see Foster 1999), the implications of his critique of Malthus are broad.

In our increasingly interconnected world, the relationship between production and environmental degradation can be seen in the context of the transnational social organization of agricultural and industrial production. This involves the control of global assembly lines, which largely involves foreign investment, and transnational corporations that are sometimes in partial cooperation with domestic firms. The process operates primarily in the interests of the firms themselves, which are largely headquartered in affluent, higher-consuming countries (Chase- Dunn 1998; Jorgenson 2003).

The findings of recent studies suggest that foreign capital penetration is a mechanism partly responsible for particular forms of environmental degradation, including carbon dioxide emissions, methane emissions, sulfur dioxide emissions, and water pollution intensity (e.g., Grimes and Kentor 2003; Shandra et al. 2004; Jorgenson 2006). It is not unusual for transnational corporations to make investments in less developed countries, which maintain lower environmental standards and policies than those found in the more affluent, high-consumption-oriented societies. A large proportion of foreign investment in less developed countries finances ecologically inefficient, labor- and energy-intensive manufacturing processes outsourced from developed countries. Moreover, power generation in the countries receiving foreign investment is considerably less efficient. This often results in increased emissions of noxious greenhouse gases (Lofdahl 2002).

Indeed, the transnational social organization of production is tied to the flows of natural resources and produced commodities between countries. Like foreign investment, international trade has become an increasingly salient issue in environmental sociology and other environmental social sciences (Lofdahl 2002; Jorgenson and Kick 2006). For example, the amount of resources a country consumes is largely a function of its level of economic development (Jorgenson 2003).

Paradoxically, nations with higher levels of resource consumption experience lower levels of environmental degradation within their borders, including deforestation and organic water pollution (Jorgenson 2003; Jorgenson and Burns 2004). International trade practices at least partially account for this paradox (e.g., Hornborg 2001; Jorgenson 2004). International trade blurs human responsibility for the environmental effects of production and consumption (e.g., Rothman 1998; Andersson and Lindroth 2001; Lofdahl 2002). Developed countries possess the international political and economic power and institutional infrastructure to achieve improvements in domestic environmental conditions while continuing to impose negative externalities (e.g., Chase-Dunn 1998; Foster 1999; Princen, Maniates, and Conca 2002).

More broadly speaking, there often is a mismatch between the logic of economics and that of ecology; while it makes sense economically to have large-scale production with many concentrations of specialized parts of the overall process around the globe, this tends to be damaging ecologically. Natural ecology works much better on a smaller scale, where waste and other by-products can be naturally recycled (Freudenburg 1990) and where production and consumption practices are more closely coupled (Foster 1999).

As we can see from the above discussion, there are numerous ways in which population processes, technology, and consumption patterns are intertwined. As a result, their influences on the environment alone and in combination are complex. Yet it is essential for social and natural scientists to continue to grapple with understanding these complexities. There is little doubt that many of the problems discussed in this research paper will get worse before they improve. Any progress that is to be made is likely to involve taking environmental problems seriously while at the same time moving the focus beyond any one single causative factor.

The specific contributory mechanisms most closely associated with environmental outcomes tend to differ by level of development of a country or region. Population processes are certainly linked with environmental outcomes, yet the level of resource consumption of a population, itself largely a function of affluence and the ways in which technologies are used, is a significant factor in environmental impact as well. Consider, for example, that per capita energy usage in the United States is over 50 times as much as in some Third World locales. Thus, although it is true that population increases have environmental consequences, it is shortsighted to stop at that observation. The ways in which populations use resources are profoundly important as well, and it is crucial to consider these factors in conjunction with one another if we are to obtain anything beyond the most simplistic of views.

That said, by virtually all projections, population will multiply significantly through at least the first half of the twenty-first century, with the most significant increases occurring in developing countries. As the human population increases, social scientists observe a number of related phenomena, such as per capita resource consumption and concentrations of population in urban areas. Higher levels of energy usage, in turn, mean greater impact on the environment, such as more extraction of fossil fuels and the degradation associated with them or more reliance on nuclear fission and, thereby, the creation of its poisonous by-products.

Increases in population and urbanization often tend to be accompanied by technological innovation, which could potentially be good for the environment (Simon 1990). Yet if history is any indicator, as new technologies are developed, they are often used to make deeper and more lasting incursions into the environment (Freudenburg and Frickel 1995). Technological innovation, thus, often has a net negative impact on the environment. As society develops in the twenty-first century, it will continue to be crucial that citizens remain vigilant about the ways in which technology is conceptualized and used.

Also of significance is the question of technological diffusion. With increasing global patterns of commerce, communication, and transportation, less developed countries are exposed to technologies heretofore typically confined to the developed world. Closely associated with technological diffusion are dramatically rising consumption patterns (e.g., Grubler 1991, 1997). Consider that with the United States currently having about 4–5 percent of the world’s population, it currently consumes about 25 percent of its energy. If every society consumed resources at the rate of developed countries, as those in North America and Western Europe do currently, the world’s resources, productive capacity, and sinks would be taxed far greater than they already are, beyond sustainable levels.

Yet consumption patterns are catching up the world over. Consider that China, the most populous country in the world, has very recently become the world’s largest consumer of a variety of commodities, from soybeans to lead and copper (Commodity Research Bureau 2005). As rapidly developing countries continue to move toward the standard of living of the most developed countries, the overall ecological impact on the planet will likely increase to heretofore unprecedented levels.

Thus, as we move well into the third millennium, we will face a number of daunting socio-environmental challenges. Air pollution and water pollution are increasingly pressing problems, which manifest themselves on a number of levels, from international to local communities. People in farming regions will increasingly have to grapple with exhaustion of topsoil in which to grow food. Worldwide, there are problems of global warming, deforestation, depletion of fresh water for drinking, and pollution of what resources there are left. Sources of food that many people have traditionally taken for granted, such as a steady supply of fish in coastal areas, are in dwindling supply.

While environmental degradation and resource depletion are worldwide problems, the specific causes and manifestations of the problems are quite distinct in different parts of the world. Certainly, the natural geography of a place— tropical, boreal, or temperate, for example—has a large effect on how people interact with the environment around them, both in terms of how they make their livelihoods and in terms of how they affect the environment. Every bit as important as the natural geography is the level of development of a country or a region—its level of affluence and technological sophistication—for this allows, and even encourages, people to have an impact on the environment.

Yet as we confront these daunting problems, a large portion of society appears to be in denial. In much of the developed world, consumption rates are at an all-time high—for example, sales of sport utility vehicles and other vehicles that consume high levels of fossil fuels and put a heavy burden on the air we breathe have increased to unprecedented levels.

There are energy technologies that are more friendly to the natural environment and thus more sustainable in the long run. However, many “alternative” fuel sources, such as solar and wind energy and hydrogen fuel cells, are not at the stage of development where they may be able to compete with fossil fuels of oil and coal in terms of costs in an open market.

Around the broad outlines we have discussed, a number of issues will continue to press society’s abilities. There will always be a need for energy sources. Inequality of access to energy and other resources will continue to be a problem. In addition to finding and making useable sources of energy and other resources, technology will need to be developed to face the inevitable consequences of making incursions into the natural ecosystem to acquire those resources.

With society moving into the twenty-first century, the challenges associated with the environment and the interrelated factors of technology, population, and patterns of consumption continue to present themselves. While societies have always faced such problems, the magnitude of environmental and technological challenges faced by the people in the twenty-first century is unprecedented in human history. There are more people than ever before with the technological wherewithal to make more profound incursions into the planet and its biosphere, consuming resources at greater rates than at any other time in human history. These factors promise to make questions regarding the environment and technology perhaps the most critical faced by society in the twenty-first century and beyond.

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