research paper on market externalities

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Externalities, scarcity, and abundance

Brett m. frischmann.

1 Department of Business and Economics, Villanova University, Villanova, PA, United States

Giovanni B. Ramello

2 Department of Economics and Statistics, University of Turin, Turin, Italy

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Introduction

Do externalities work and matter differently in a world of scarcity vs. a world of abundance? In this article, we critically examine the economic phenomena of externalities. The concept of externality, an important idea in economics and law, is useful in exploring the complex and dynamic relationships between resource supply and human flourishing within various sociotechnical systems.

First, we define the basic concept and explain why it is fundamental to economic analysis of complex social environments Second, we briefly survey the intellectual history of externalities with the goal of tying together a few different strands of economic theory and providing a roadmap for a general theory of externalities. This discussion highlights a latent conflict between those who pursue and those who resist perfectibility (optimization) of social systems by internalizing externalities. Third, we compare externalities in worlds of scarcity and abundance.

This article develops the theoretical framework, including a brief intellectual history and notes toward the development of a general theory of externalities. As a conceptual tool, externalities enable one to identify and examine social interdependencies and to map their causes and consequences. Externalities provide evidence of social demand for governance institutions. This descriptive utility can and should inform normative analysis, the design of governance institutions, and comparative institutional analysis. We also raise a series of (mostly empirical) questions that should frame comparative institutional analysis and evaluation of different externalities in the digital networked world.

We focus on the scarcity and abundance of knowledge resources and the (technological) means for participating in the production, dissemination, and modification of such resources. In the real, necessarily imperfect world where abundance and scarcity vary across resources, people, and contexts, externalities persist, indicate social demand for governance, and inform comparative analysis and design of governance institutions.

JEL classification

D62, B52, D02.

1. Introduction

Do externalities work and matter differently in a world of scarcity vs. a world of abundance? Over the past decade, many prominent scholars and thought leaders have argued (hypothesized) that increasing abundance of various types of knowledge resources and the technological means for participating in the production, dissemination, and modification of such resources will lead to substantial impacts, changes, and even disruptive transformation of existing political, economic, and social systems. This article does not empirically test this rather broad claim. Instead, we presume there is some inevitable truth to the generic claim, which anyone living in the twenty-first century can appreciate, and focus more directly on understanding the mechanisms, namely, in how scarcity and abundance of knowledge resources shapes political, economic, and social systems and, as we shall see, vice versa. This inquiry forces us to interrogate the conventional economics of externalities.

The concept of externality, an important idea in economics and law, is useful in exploring the complex and dynamic relationships between resource supply and human flourishing within various sociotechnical systems. Unfortunately, the externality concept is easily confused in making prescriptions. For example, economists often consider externalities to be a prime example of market failure (Papandreou, 1994 ). As we explain below, this is a bad heuristic. In reality: Externalities are sometimes evidence of market failure and other times evidence of market success. Furthermore, externalities sometimes are not primarily about markets failing (or succeeding) but instead concern political or other social systems failing (or succeeding) (Claassen, 2016 ). Not surprisingly, a bad heuristic can lead to bad prescriptions to remedy supposed failures.

Before we examine how externalities work and matter in worlds with varying degrees of scarcity and abundance, we provide a series of clarifications to help avoid the problems that plague conventional theories. We explain that externalities (i) are system-independent, (ii) always concern the interdependent and functional relationships between people and environments (resources, both natural and built), (iii) vary according to the set of values people have, and (iv) often, though not always, give rise to social demand for governance. As institutional economists recognize, externalities and institutions are inexorably intertwined. Yet governance institutions, which are by no means limited in focus to internalizing externalities, are themselves socially constructed resources that comprise and shape the built environments within which people live and develop their beliefs, preferences, and capabilities. This unavoidable fact adds a layer of complexity to the analysis that we do not fully describe in this paper and thus leave for other work. 1 But we mention it because it is relevant to understanding how externalities and corresponding governance institutions work and matter differently in a world of scarcity vs. a world of abundance.

The real world is necessarily imperfect. It is complex and messy. Scarcity cannot be eliminated, and thus, a “world without scarcity” will never exist and can only be theorized. Nonetheless, which resources are scarce and to what degree does change over time and is a critical issue. Economics generally acknowledges these facts. In Part 2, we discuss the economics of externalities with these facts and the interdisciplinary audience of this journal in mind. 2 In Part 3, we engage the hypothesis noted above regarding abundance. First, we briefly consider the abstract idea of a world of absolute abundance (without scarcity) and note how Ronald Coase used and others have abused this idea. This discussion situates our analysis in the broader themes of the Special Issue of Frontiers. Second, we consider the more realistic idea of a world in which specific sets of knowledge resources are increasingly abundant. Such a world can exist, and in such a world, externalities matter.

In our modern digital networked world, externalities are, in fact, ubiquitous. We hypothesize that there are more externalities than ever in human history, social interdependence is at an all-time high, and social demand for governance is unmet and on the rise. Wishful thinking and appeals to abundance-enabled innovation, disruption, and democratization too easily distract, dissemble, and ultimately, disable comparative analysis and design of appropriate governance institutions. Accordingly, in Part 3, we offer a series of (mostly empirical) questions that challenge such appeals and frame interdisciplinary research needed to support comparative institutional analysis. 3

2. Externalities in a world of scarcity

2.1. definition and an abbreviated intellectual history.

Most economists would agree on a rather standard and common definition of externalities that can be put in the following terms:

Externalities are benefits or costs realized by one human being as a consequence of another human being's activity without a full accounting of the effects by the parties . 4

Based on this definition, one can say that externalities are rather familiar. We generate and realize externalities daily by virtue of our experiences in an interdependent society. 5 Consider how many of your actions have small but nonetheless real effects on others around you. Many effects are small in magnitude and seem trivial—say, the effects of one person's loud cackling laugh on others trying to read at a coffeehouse. Such effects may add up and become more significant if persistent or widespread—if the cackler persists for a long time, perhaps every morning... or consider a person chatting loudly on her cell phone every morning on the public transit bus... or a person that maintains a beautiful flower garden to the benefit of those who pass by on the way to the bus... and so on. Textbook examples are legion. Negative and positive externalities are ubiquitous (Laffont, 2008 ).

Despite such familiarity and general agreement on the basic definition, the meaning and relevance of externalities has been contested in economics for many years. Acknowledging that “externality is an ambiguous concept,” Harold Demsetz suggested that “every cost and benefit associated with social interdependencies is a potential externality” (Demsetz, 1967 , p. 348 [italics added]). In his view, externalities exist only where benefits or costs are not taken into account by parties because “the cost of transaction in the rights between the parties (internalization)... exceed[s] the gains from internalization.” In a similar vein as Demsetz, Kenneth Arrow insisted that the existence or non-existence of externalities is a function of the relevant institutional setting, incentive structure, information, and other constraints on the decision-making and exchange possibilities of relevant actors (Arrow, 1970 ; see also Papandreou, 1994 , p. 13–68). Arrow connected externalities to the absence of a functioning market (Arrow, 1970 , p. 59–67), essentially equating an externality with an incomplete or altogether missing market (Cornes and Sandler, 1996 , p. 40–43).

In mainstream economics, externalities are one possible cause and even represent one form of market failure (Bator, 1957 ; Laffont, 2008 ). Externalities are one reason given in most economics textbooks to explain how markets fail to allocate resources efficiently. This has been standard, at least since Paul Samuelson's seminal work on public goods (1954).

The perceived problem is that externalities are not fully factored into a person's decision about whether, how, and how intensely to engage in an activity, and consequently—that is, as a result of the incomplete consideration, externalities may have a distorting effect on market coordination and allocation of resources. The linking of externalities to market failure suggests the following hypothesis:

• H1: Too few (many) resources will be allocated to activities that generate positive (negative) externalities because persons deciding whether and how to allocate resources to such activities will fail to account for the full range of benefits (costs) .

And the following (counterfactual) hypothesis:

• H2: If the unaccounted-for benefits (costs) were taken into account, or internalized, the actors would behave differently, reallocating their resources in a more efficient manner .

Distortions manifest both on the supply side, in terms of reduced incentives to invest in what would otherwise be optimal supply, and on the demand side, in terms of lost signals about what consumers want and where investments should be directed (Laffont, 2008 ). The “lost signals” description follows from the Arrow's notion of externalities as missing markets or unpriced exchanges.

We can describe the supposed market failure at two different levels of abstraction. First, at a micro level (partial equilibrium, see, e.g., Buchanan and Stubblebine, 1962 ), it may be seen as the consequence of an imperfection in the market for some specific good or service generated by or otherwise attributable to a specific activity. One prominent example involves public goods (Samuelson, 1954 ).

Consider an example: silence on public transportation . Silence (noise) can be quite valuable (costly), is non-rivalrously consumed, yet is often underproduced (overproduced). Individual producers contribute without fully capturing or accounting for the benefits (costs) realized by others; there typically is no market exchange. The shared environment of public transportation is easily congestible, however, by one person or a few. While those who value jointly produced silence might coordinate with each other and even engage in an exchange with those who would break the silence, such transactions are far and few between. Social norms and other informal governance mechanisms may work in some contexts, but not in others. 6 Legal rules might even be adopted. But at what cost? A comparative analysis of institutions available to solve this collective action problem can get quite complicated. The point here is simply that actors being quiet and noisy may generate externalities as their actions generate benefits and costs for others in their vicinity. Whether or not any given level of silence/noise is optimal is highly contextual and may be difficult to assess. In this case the very notion of optimality depends on a partial equilibrium analysis, which essentially means, pretending all other markets and non-markets work perfectly. Below, we discuss some shortcomings of this style of analysis (see also Frischmann, 2012 , p. 53–57).

Second, at a macro level (e.g., general equilibrium, see Arrow, 1970 ; Papandreou, 1994 ), the supposed market failure may be seen as an imperfection in the market for markets . A market may be missing altogether (Arrow, 1970 ; Berta, 2017 ). There are many reasons why this might be the case. We discuss some below. The basic idea is that markets are themselves a complex public good that must be supplied by people. Markets themselves—through the activities of market participants—generate many different types of positive and negative externalities, and as recent research has examined, markets are often a form of knowledge commons (Frischmann et al., 2014 , 2017 ; Dekker and Kuchar, 2021 ). And so, like other public and social goods, markets themselves may be underproduced.

Demsetz ( 2008 ) argued the market for markets is presumptively an efficient means for assessing when the benefits of internalization exceed the costs of internalization, and thus, markets, like property rights, will come into being when it is efficient to internalize externalities. Specifically, he said: “Just as the market dictates that there will be no good X if the cost of producing X exceeds what people are willing to pay for it, so the market dictates that there will be no market if the cost of producing the market exceeds what people are willing to pay for it” (Demsetz, 2008 , p. 131).

Frischmann ( 2009 ) replied that this view mistakenly “equates supply and demand for property rights [and] other internalization mechanisms such as regulation... with a market.” Demsetz extended partial equilibrium assumptions to the market for markets, which is not justified since it only pushes the analysis up a level of abstraction and does not deal with the complex interdependencies of externalities flowing within and between markets and non-markets (or market and non-market systems). Frischmann ( 2009 , p. 815 [italics added]) explained:

Participants in the market for a market for X are not likely the same (complete set) as the participants in the market for X , nor are the third parties affected by the actions of either set of market participants the same. We cannot assume that everyone participates in each market or in some macro-market-for-potential-markets without simply assuming away the notion of third-party effects altogether.

The market for markets frame presumes the market system is the default social system for social coordination and governance. In reality, political and other social systems play a (more) significant role in supplying governance institutions, including those necessary for markets.

The existence of silence (noise) on the train, for example, is not well explained by an economic analysis of whether a market exists and whether transaction costs for creating such a market are too high. Of course, one can contrive a model or tell a story about non-existent property rights and high transaction costs, but such analysis borders on tautological. Most people detect the handwaving and intuitively know that in most cases, a better explanation is rooted in social norms and cultural attitudes. 7 Economic analysis has a lot to offer, especially comparative institutional analysis and economic sociology. But much more detail is needed than facts about transaction costs, property rights, and the (non)existence of a market. 8 That a (luxury) market for silent travel, e.g., quiet cars on trains, may exist alongside other governance arrangements does not undermine the point.

Assuming (for now) the two hypotheses are true and externalities determine market failure, then how, according to conventional economic thinking, should society address the resource misallocation problem? For some time, most economists accepted Pigou's view that the government ought to “intervene” via the tax or regulatory system and force externality-producing agents to fully account for their actions (Pigou, 1932 ). Thus, producers of negative (positive) externalities, such as pollution (education), should be taxed (subsidized) at a level that aligns private and social costs (benefits) (Cornes and Sandler, 1996 , p. 72–78).

Coase ( 1960 ) challenged to the “Pigovian tradition” and gave credence to property rights as an alternative to the Pigovian solutions of government taxation or regulation as a means of dealing with externalities (De Meza, 1998 , p. 270–73). Coase first suggested that in a world without transaction costs, which he referred to as “costs of market transactions,” all that would be needed for the market to function properly are well-defined property rights. 9 In such a world, regardless of how property rights are assigned, everyone who might be affected by use of the resource to which the property right applies would bargain and (re)allocate rights in a manner that maximizes social welfare (Coase, 1960 , p. 15–19; De Meza, 1998 , p. 270). Of course, this theorem, sometimes referred to as the Coase Theorem to Coase's dismay, only holds in a world without transactions costs, which is not the world we live in (Coase, 1960 , 1988 ; Ellickson, 1989 ; De Meza, 1998 ; Ramello, 2011 ; Frischmann and Marciano, 2015 ).

Coase mainly intended to emphasize the importance of considering transaction costs when comparatively evaluating institutional solutions to perceived market failures (Frischmann and Marciano, 2015 ). Coase anticipated a role for government above and beyond defining and enforcing property rights, but he thought that role should be evaluated contextually with a full understanding of the reciprocal nature of interdependent relationships 10 and without a reflexive invocation of externalities to justify government action (Coase, 1960 , p. 18; De Meza, 1998 , p. 275). Buchanan and Stubblebine ( 1962 , p. 381) agreed: “There is not a prima facie case for intervention in all cases where an externality is observed to exist.”

Buchanan and Stubblebine ( 1962 ) introduced the idea of relevance , by which they divided externalities worthy of attention and internalization from those deemed irrelevant. An externality is relevant only if its removal via internalization is Pareto improving 11 ; this does not mean that internalization is necessarily justified because an evaluation of whether or not to internalize would turn on the costs of internalization, which vary according to technology, institutional context, and other factors. The point is more basic. The relevance/irrelevance distinction depends, in Buchanan and Stubblebine's analysis, on whether net gains can be made from a hypothetical, costless “trade” between parties. Absent such gains, internalization is not worth considering for it would not matter to the generating actors' behavior or incentives.

Demsetz ( 1967 ) took a different approach and advanced a theory of property rights evolution where imperfectly defined property rights improve and evolve to meet societal demand for the internalization of externalities. 12 . By definition (within economics, at least), property rights can be perfectly defined only in a world without externalities. In such a world, the range of “sanctioned behavioral relations among economic agents in the use of valuable resources” is completely and unambiguously delineated (Libecap, 1994 , p. 145; Demsetz, 1998 ). As Libecap ( 1994 , p. 145) explains, “In the limit, if property rights are so well defined that private and social net benefits are equalized in economic decisions, benefits and costs will be entirely borne by the owner.”

By insisting on property rights and institutions, Coase, Buchanan and Stubblebine, and Demsetz all meant to emphasize the importance of institutional means (solutions) to deal with external effects. In the absence of transaction costs, Coase explained, there is no need for government intervention because individuals can bargain and devise solutions to deal with the interdependencies that exist between them. 13 Similarly, Buchanan ( 1965 ) claimed that individuals could devise “clubs” that allow individuals to internalize externalities and produce (local) public goods. If property rights are correctly defined, then externalities would be internalized (dealt with in the club). Yet, transaction costs exist, and clubs cannot always be built. When they can, they are not always perfectly efficient. Not surprisingly, the real world is awash in imperfectly defined property rights and externalities (Demsetz, 1998 , p. 144; Epstein, 2002 , p. 520; Frischmann, 2004 , p. 967; Frischmann and Lemley, 2007 ).

2.2. Toward a general theory of externalities

In this section, we question and aim to correct some oversimplifications in the conventional theories. We begin with the basic definition: Externalities are benefits or costs realized by one human being as a consequence of another human being's activity without a full accounting of the effects by the parties . The definition entails three parts, each of which merits brief reflection:

Benefits/costs realized by a human being encompasses genuine adjustments in a person's welfare, interpreted for our purposes broadly to include wellbeing, capabilities for human flourishing, and other conceptions of values. While economics tends to prefer working with welfare measured in specific ways, one can reasonably describe externalities in terms of many different conceptions of benefits and costs that include human capabilities. 14 As a consequence of another human being's activity requires a causal, functional connection between actions and consequences. Actions occur and cause effects in and through shared environments, physical and otherwise. The causal relationship and environmental conditions matter. A full accounting of the effects by the parties requires effects be factored into decision making about the activity that generates the effects as if the parties are one party or, put another way, as if the decision is mutual. It thus requires more than mere awareness of or even knowledge about the effects by one or more of the parties.

The existence of an externality signifies an incomplete accounting of effects. There are many potential reasons. Incomplete accounting may be due to a lack of awareness, appreciation, or understanding (hereinafter “knowledge”) of how one person's actions generate consequences for others. While we group awareness, appreciation, and understanding together under knowledge for expository convenience, there are subtle and important differences between these states of mind, how they contribute to an incomplete accounting, and the types of governance mechanisms (interventions) that might enable a complete accounting. For example, institutions focused on transparency and notice may provide awareness but fall short with respect to appreciation and understanding. Knowledge about the dynamic relationships between actions, mediating environments, and consequences for other people is sometimes in the realm of common sense—as in the case of a person speaking loudly on the public train—but other times may be much more complicated—for example, when one contributes to “anonymous crowding,” a type of congestion, on shared networks (Cornes and Sandler, 1996 , p. 355; Arruñada, 2017 ; Frischmann et al., 2019 , p. 222–23).

Yet even with the necessary knowledge of such complexities, incomplete accounting may exist due to a lack of mutual concern (hereinafter “mutuality”). Making decisions as if parties are in fact one party is at the core of what economists mean when they refer to internalization. Theorists of collective action might instead use the words coordination and cooperation. A simple Prisoners' Dilemma provides a decent illustration. Even if both players are fully informed about the payoff structure and the consequences of their individual decisions, the dominant strategy eschews mutuality. Knowledge is not enough . The accounting is incomplete because of the lack of mutual concern. Institutions can provide a means for escaping the dilemma.

Mutuality is often socially constructed. It generally, but not always, requires some form of governance. Mutuality—internalization, cooperation, coordination, incentive alignment—can be genuine; that is, it does not need to be an as if condition. It can exist by virtue of a contract or joint membership in a common enterprise, such as a club, partnership, or corporation. It also can exist because the parties are very closely related, for example, family members. In the scenarios first noted, different governance institutions may effectively join parties such that one party making a decision that has a consequence for another should account fully for the effects, provided the actor has sufficient knowledge to do so. But even in such scenarios, mutuality is not guaranteed or inevitable (which is why we used the words “can” and “may”).

As if scenarios are legion. As if mutuality exists, for example, when social norms induce genuine consideration of others, including strangers, before acting in a manner that might affect them. Similarly, strict liability rules effectively require actors to make decisions in this fashion. As if mutuality also arises when property rights and other legal rules provide mechanisms for affected parties to seek recourse from actors who cause them injuries. There are plenty of examples to tease out, but the basic point is made.

Externalities mean an incomplete accounting; internalization of externalities entails a full accounting. Both components— knowledge (awareness, appreciation, and understanding) and mutuality (actual or as if)—matter. Different institutions can be designed to support one or both components. When engaging in comparative institutional analysis and assessing demand for governance, one must consider both components (see also Arruñada, 2017 ).

Our basic correction to the conventional economic theory about externalities is to cast aside the externality as market failure framing and replace it with the following.

First, externalities are fundamentally a product of human beings (inter)acting with(in) environments . Human beings are actors/agents with various capabilities and characteristics. They have their own independent will (beliefs, preferences, values, and intentions) and social relationships, and they are necessarily situated and even embedded in complex environments that shape their development and interactions. Multiple, complex, overlapping, and interdependent resource systems constitute those environments—the natural environment is one type and socially constructed (built) environments are another. 15 These basic facts about the framing matter because they provide the contextual details or parameters necessary for identifying and evaluating externalities: Relevant parameters for such analysis include, inter alia , actors, actions, causal relationships, consequences/effects, environmental mediating factors, and relationships.

Second, externalities are not exclusively a market phenomenon. Rather, externalities arise as relevant phenomena in all social systems, including but not limited to markets. In various social contexts, incomplete accounting can lead to third-party effects. Externalities serve an evidentiary function by indicating demand for governance, which might be supplied in various forms by participants in market, political, or other social systems.

Third, externalities are not failures per se . Counterintuitively, externalities can be and often are evidence of successful operation of social systems and therefore do not require any internalization. For example, markets regularly generate externalities that need not and should not be internalized. Knowledge production in markets is a prime example where spillovers are widespread and socially desirable (Frischmann, 2007 ; Frischmann and Lemley, 2007 ; Ramello, 2011 ). This is success. The same can be said about political, academic, and other social systems. Success or failure depends on the contextual details.

The two hypotheses (H1 and H2 above) are thus sometimes valid, depending on the context, the activities, resources, technologies, and governance institutions, among other things. The critical empirical question, then, is to figure out when the hypotheses hold because that indicates there is a social dilemma, demand for governance, and an opportunity for improvement by internalization.

Fourth, internalization is no panacea. Internalization of externalities can be a solution when there is a problem to solve, but it also can be a problem to avoid when the two hypotheses do not hold. Knowledge and innovation are particularly useful examples. It is not just that producing and sharing knowledge can generate endless ripple effects that are too costly to internalize; it's that the ripple effects are often precisely the point. In fact, even if cheap, internalization can cause distortions that undermine the generation of socially valuable ripple effects, including cumulative innovation and cascading spillovers (Arrow, 1962 ; Scotchmer, 1991 ; Frischmann, 2009 ). For example, if the inventor of the microscope captured the full social value of the invention, it would reduce the incentive for countless scientists to make innumerable discoveries that are in the aggregate far more valuable (Arrow, 1962 ; Lemley, 2005 ; Frischmann and Lemley, 2007 [collecting sources and historical examples]). More generally, for infrastructural public goods for which a significant fraction of surplus is attributable to productive (re)use, internalization may affirmatively reduce social welfare (Frischmann, 2012 ). Ultimately, the case for and against internalization depends on the context and the scope of the analysis.

One way to see the third and fourth points is to reconsider Buchanan and Stubblebine's analysis of relevance. Buchanan and Stubblebine suggest that an externality is relevant only if its removal via internalization is Pareto improving; otherwise, it is irrelevant and need not be considered. The assumption is that the parties would not transact because there are no gains, and so it must be deemed irrelevant. There is no social dilemma, no problem to solve; internalization is inefficient. But what if their joint actions generate external effects that make internalization Pareto improving and thus worthwhile, although not market accountable?

For example, suppose A makes noise whistling on the bus and disrupts B, who cannot concentrate while A whistles. B would be willing to pay $1 to A if A would stop. This is not enough, however. A enjoys whistling and would only be willing to stop and forego such enjoyment for $2. According to Buchanan and Stubblebine, since there is no gain to be made via transaction, the externality is irrelevant. It need not and should not be internalized, even if internalization were itself costless. But suppose B is a writer, and on the bus, she writes interesting threads on Twitter that hundreds of followers enjoy (~$0.02 per follower). Further suppose that some fraction of her followers shares the threads with their followers, and that some fraction also adds their own content to the threads. We could go on extending the scope, the types of activities, public goods produced and shared, and interdependencies. We could change the medium (social technology of interaction) too. The point is that (ir)relevance and the corresponding economic case for internalization for each externality (externality-generating activity) depend entirely on how many interdependent market and non-market interactions one incorporates into the analysis. No matter how much we extend the analysis to markets, we cannot capture all social interdependencies and associated dynamics unless we make society coincide entirely with the market. 16 Partial equilibrium analysis may be useful in making things tractable and working up a model to examine specific interactions, but it can be dangerously myopic (Frischmann, 2013 ).

One might wonder whether this example proves too much. It would seem to apply to countless examples of externalities, such as environmental pollution that inhibited an author from writing. Our example is one of millions we could describe. Silence is a public good that is valuable to some meaningful degree because it affords people opportunities to be productive in certain ways including but by no means limited to writing. 17 Frankly, a healthy physical environment (free of pollutants) similarly affords people opportunities to be productive in certain ways including but not limited to writing. How health impacts productivity matters. It is structural. The argument applies to countless examples of externalities. That is the point.

Finally, a fundamental shortcoming made when examining externalities is to couple partial equilibrium analysis (and associated assumptions) with prescriptions focused on the pursuit of optimality or the perfectibility (optimization) of social systems. To develop this argument, we return to Paul Samuelson's seminal work on public goods.

Samuelson ( 1954 , p. 387) suggested that since public goods simultaneously enter the “indifference curves” or “consumption functions” of many people, optimal production would have to account for the aggregate value for the consuming population. Thus, investment in production of a public good should expand so long as the aggregate marginal benefit to consumers exceeds the marginal cost. The optimality condition is framed in terms of marginal rates of transformation and substitutions as follows: Public goods production should expand until the marginal rate of transformation equals the sum of the marginal rates of substitution.

Accurately measuring demand and achieving optimality are difficult because consumers may act strategically and understate their actual preferences hoping that others will bear a greater proportion of the costs. This is known as the preference revelation problem. Competitive markets struggle with measuring demand for public goods, and while government could solve the demand revelation problem in some contexts through voting and political processes rather than market processes, Samuelson recognized that all of these processes are imperfect and thus optimal production would be elusive. 18

The Samuelson condition indicates whether public or private investment in public goods production is justified, and effectively that evaluation is situated at the margin between investment in further public goods production and alternative investment opportunities (e.g., in private goods production). Here is what that means: Imagine you must evaluate a stream of potential investments. Specifically, you must decide whether to expand investment in public goods production. Expanding investment might mean investing more in an existing public good to improve its quality or investing in a new public good. Either way, the point is the same. For each potential public good investment, one must compare the aggregate benefits to the production cost, which includes the cost of capital and opportunity costs associated with alternative investment opportunities (i.e., rate of substitution).

In the basic model discussed thus far, the basis for measuring benefits to be aggregated is consumer preferences or willingness to pay for the public good in question. This model effectively assumes a single market, the public good market. Even if we assume consumers do not actively conceal their true preferences in a deliberate effort to free ride, demand measurement problems may persist, and optimal production may remain practically impossible.

The demand side analysis gets quite complicated when the public good is used productively, rather than merely consumed, and such productive use itself generates externalities. 19 Recall our bus-riding author who used quiet/silence (public good 1) productively to produce Twitter threads (public goods 2, 3, … n), and followers who then shared those goods and by adding their own comments produced others (public goods n+1, n+2 …). Even if consumers cooperate and accurately reveal their preferences for some of those public goods, those preferences do not account for various third-party and structural effects. Unless externalities are internalized throughout the entire system (incomplete markets are completed, missing markets are made functional, etc.), which is impossible in the real-world, we must acknowledge and grapple with systematic demand side problems of both types—distortions associated with measuring actual consumer preferences and distortions associated with externalities (Again, there is nothing special about this example. We could describe countless other familiar examples with the same basic structure.).

The demand measurement problems posed by measuring actual consumer preferences and significant cascading external effects call into question the utility of marginal analysis and focusing on optimality conditions. Samuelson anticipated this point in an essay reflecting on his public goods theory:

Having called attention to the nature of the [first demand measurement] difficulty, I do not wish to be too pessimistic. After all, the world's work does somehow get done. And to say that market mechanisms are non-optimal, and that there are difficulties with most political decision processes, does not imply that we can never find new mechanisms of a better sort (Samuelson, 1958 , p. 334). [It] should be possible for the theorist to go beyond the polar cases of (1) pure private goods and (2) pure public goods to (3) some kind of a mixed model which takes account of all external, indirect, joint-consumption effects. I shall not write down such a mathematical model. But if I did do so, would we not find— as Pigou and Sidgwick so long ago warned us is true of all external economies and diseconomies -that the social optimum could not be achieved without somebody's taking into account all direct and indirect utilities and costs in all social decisions? (Samuelson, 1958 , p. 335; emphasis added).

Now some may read this passage and believe Samuelson was making the case for a centralized decision maker such as the government. But this seems a stretch. Samuelson recognized the importance of external effects and the severe limits they posed on efforts to perfect both market and government systems and thus to achieve optimal production of public goods. Recognizing those limits, he suggests a continued search for “new mechanisms” might be worthwhile. His reference to Pigou and Sidgwick and “somebody's taking into account all [effects] in all social decisions” implicitly acknowledges that the fundamental limit is a full accounting, which as we explain above, entails both knowledge (awareness, appreciation, and understanding) and mutuality.

We live in a very complex second-best world evidenced by the prevalence and variety of external effects (Lipsey and Lancaster, 1956 ). Attempts to perfect one market should be expected to cause unpredictable and often harmful distortions in many other markets and non-markets. Those who are optimistic about the perfectibility of social systems, including markets, may believe that abundant data and powerful computational technologies will reduce complexity, eliminate externalities, and enable optimization across markets and non-markets. However, there is no empirical support for such beliefs. To the contrary, social interdependencies multiply, complexity increases, and externalities abound (We discuss this claim further below in the context of the Internet, digital networked technologies, and abundant knowledge).

Pursuing optimality in this case is quixotic . We should set aside optimality conditions and instead focus on how to improve market, government, and other social systems (and even new mechanisms) for the bulk of investments that are not at the “edge” in terms of being the last marginal projects that would satisfy the Samuelson conditions. We simply know too little about the territory leading up to the edge. To make the analysis tractable, we have to assume away (and thus ignore) too much.

Speech is a useful example. Speech is a communicative activity that regularly generates externalities, both positive and negative. Speech generally entails the sharing of public goods (ideas, facts, stories, rumors, falsehoods, knowledge, etc.), and such sharing often has direct and indirect effects. Speech affects social interdependence in many ways. Not surprisingly, we do not aim to optimally produce speech. It makes little sense to rely on governments or markets to optimally produce speech. It is simply too difficult to even begin measuring demand, and not just because some consumers will misrepresent their preferences in the hope of free riding. The knowledge requirements alone are hard to fathom, and mutuality is, in many cases, impossible. There are too many complex interdependencies. Internalization is not the overriding social objective, and while a relevant consideration, transaction costs are not sufficient explanation. Speech externalities are expected and encouraged. Indeed, abundant speech externalities are one of the foundational elements of a democratic society, especially one committed to pluralism.

3. Scarcity, abundance, and externalities

Recall the motivating hypothesis noted in the Introduction (and drawn from the themes of the conference and special issue) that increasing abundance of various types of knowledge resources and the technological means for participating in the production, dissemination and modification of such resources will lead to substantial impacts, changes, and possibly even disruptive transformation of existing political, economic, and social systems. Our (modest) claim is that, properly understood, the concept of externalities remains useful in exploring the complex and dynamic relationships between resource supply and human flourishing within various sociotechnical systems.

In previous sections, we described externalities as economic but also social phenomena. We had the real world in mind, and that means, we have been talking about how externalities work and matter in a world of scarcity. We now turn our attention to the question of how externalities work and matter in a world without scarcity , to use the phrase suggested in Mark Lemley's provocative 2015 article, IP in a World Without Scarcity .

There are a few ways to understand the world without scarcity. 20 We discuss three.

First, we imagine a world without scarcity , which we could also call a world of absolute abundance. Now this is easy to say but hard to describe. What would it mean for all resources to be abundant? It could mean that all resources are (somehow) freely and limitlessly available. We might begin to venture into science fiction in trying to figure out how to describe such conditions, but we need not go that route. In economics, scarcity and abundance are a function of supply and demand. So long as supply well exceeds demand, scarcity may not be a relevant concern. But short of imagining a world with a very small population relative to available resources (cf. Hardin, 1968 ) or a population with very small demands (Frischmann and Selinger, 2018 [describing a world in which billions of people are made maximally happy at low cost by engineering their preferences]), it is difficult to take seriously the idea of a world without scarcity. One way or another, environmental resources, raw materials, attention, time, and many other resources will remain finite, in demand, and thus scarce.

Second, we revisit the Coasean world of zero transaction costs and perfect information. This is not a world without scarcity, but it is another idealized world. We mention it here because many of the flaws in law and economic reasoning based on the supposed Coase Theorem could reappear in this context. Notably, the Coase Theorem was Stigler's invention (Stigler, 1966 , p. 113), not Coase's (Frischmann and Marciano, 2015 ). Generations of law and economics scholars have invoked the Coase Theorem and the ideal of a world without transaction costs to set baselines in theoretical models and frame prescriptive arguments about property rights. But this line of (law and) economics analysis often misses Coase's fundamental point. As Frischmann and Marciano ( 2015 , p. 348–349) explain:

Coase had little faith in the toy model of a zero transaction cost world; he did not champion property rights or any particular social arrangement over any other. Rather, he critiqued partial analyses and emphasized that it is “desirable that the choice between different social arrangements for the solution of economic problems should be carried out in broader terms [than the value of production as measured by the market] and that the total effect of these arrangements in all spheres of life should be taken into account”.

Zero transaction costs, like zero scarcity, is an analytical red herring. A better, more realistic economic analysis must acknowledge the prevalence and importance of transaction costs and scarcity and focus on comparative institutional analysis.

Third, and more in line with the motivating hypothesis, we focus on specific resources and evaluate what it means for them to become more abundant. Once we abandon utopian dreams of ideal worlds and embrace reality, we must recognize that scarcity will remain relevant. The key economic questions concern which resources are scarce, which are abundant, and how do we govern their production, use, distribution, and so on. Of course, answering these questions necessarily requires careful consideration and evaluation of social interdependencies, which, as we have explained, are contingent upon the complex, dynamic relationships among people and their (resource) environments.

Thus, not surprisingly, externalities will remain and remain salient. The existence of externalities tells us different things, depending on the context. First, externalities might be evidence of failure or success of different social systems. This interpretation depends on the context and thus requires empirical testing of the two hypotheses (H1 and H2). Second, and related, externalities might manifest social demand for governance. There may be an opportunity to improve the state of affairs for those people who have interdependent relationships. Such an evaluation depends on their values and relationships and the effects of their actions. Third, externalities might indicate a lack of mutuality or relevant knowledge. This information would help in the design and comparative evaluation of institutions.

The motivating hypothesis about increasing abundance presses us to consider a series of questions about any externalities. In designing, comparing, and evaluating institutions to address governance challenges raised by externalities, we should ask:

• How are the externalities created?
• Which activities generate them?
• What economic, technological, social, and environmental conditions support these activities?
• What types of externalities are created?
• How are the externalities distributed to or realized by third parties?
• Do third parties realize costs and benefits cognitively with awareness and appreciation (and perhaps a willingness to pay if a market were to form), or are the costs and benefits realized more passively, taken for granted, or perhaps appreciated only vaguely?
• What are the relevant social systems? Do we need or want a market?
• Can we differentiate between types of externality-producing activities and types of externalities in a manner that is relevant to decision making despite problems with quantification and measurement?

These are representative questions; the list is by no means exhaustive. These are not arbitrary, however. The questions outline contextual details necessary for identifying different types of externalities and mapping parameters relevant to evaluation and institutional design (e.g., actors, actions, causal relationships, consequences/effects, environmental mediating factors, and relationships).

Returning to the motivating hypothesis, we might ask: What does increased abundance of knowledge resources mean for intellectual property laws that historically have been designed to create artificial scarcity and thereby facilitate markets? Lemley ( 2015 ) argued that the premises, purposes, and design of intellectual property laws needed to change in light of his predictions of increased abundance. He suggested that the Internet presaged 3D printing, Synthetic Biology and Bioprinting, and Robotics, that these technologies promised to eliminate scarcity (increase abundance) by enabling a much larger number of people—perhaps everyone—to access and use effective means of producing a wide range of intellectual and physical goods. Desai and Magliocca ( 2013 ) and Desai ( 2014 ) considered how digitization enabled decentralized production, lowered transaction and other costs, and disrupted existing business models and technological platforms. With a focus on 3D Printing, these scholars examined how markets and legal systems evolve in response to abundance, resolving some social dilemmas while creating others. Notably, Desai ( 2014 ) rejects the ideal of a world without scarcity, instead recognizing the scarcity will persist and continue to drive economic activities.

Another wave of technologies promising to destroy scarcity and generate abundance has emerged since 2015. We could discuss a range of supposedly smart tech or blockchain or NFTs or the metaverse or others. But it is not necessary to evaluate these or any other technologies that make grandiose promises about “democratizing” innovation, knowledge production, or other related activities (Marciano et al., 2020 ). Instead, we can make our point more simply if we focus on the Internet and consider why and how scarcity inevitably persists and what follows from that basic observation.

The Internet provides and shapes opportunities for individuals, firms, households, and other organizations to interact with each other and participate in various social systems. The scale and scope of possible and actual social interactions is staggering. To put it simply, a person can easily (with a click of button, at zero marginal cost) instantaneously communicate an idea to millions of people around the world. The idea can be about nearly anything. It can take various forms and be distributed in various media. It can generate positive and negative effects. It can be part of a continuous stream of interactions. And so on …

Everything that occurs on the Internet entails the communication of data between computers at the “ends” of interconnected networks. The bottom line, for our purposes, is that every interaction involving the Internet involves the generation and sharing of public goods (data), which are inputs into the production of public and social goods at the application, content, and social layers of the Internet ecosystem. Externalities are incredibly varied and ubiquitous (for details, see Laffont, 2008 ; Frischmann, 2012 ; Frischmann and Selinger, 2018 ).

In line with the motivating hypothesis, it is perfectly reasonable to assert the following: Due to the Internet, more people have access to more data, knowledge, speech, and other intellectual resources as well as more means of producing and sharing such resources with others than ever before in human history . These public good and infrastructural resources are increasingly abundant such that scarcity may seem nonexistent. But that is not really the case. Scarcity remains. In fact, scarcity of some resources has risen along with the abundance of others. Recall that scarcity and abundance depend on supply but also on demand. There may be an incredible, growing supply of intellectual public goods and infrastructural resources, but at what costs? On the supply side, inputs needed to produce and sustain such abundant supply may be scarce and increasingly so. Energy, time, and attention, for example, are rivalrous resources that for many suppliers (producers, curators, distributors, etc.) are increasingly scarce. On the demand side, what is the social demand for such resources? Do people want or need them? Do people access and use them? Again, at what costs?

That the Internet makes production and distribution incredibly easy and cheap—even costless—does not mean that consumption and productive use are costless. Counterintuitively, overabundance 21 generates and exacerbates scarcity, as people must invest scarce resources (again, time, energy, and attention come to mind) to manage their affairs in a world drowned in data and digital networked technologies that mediate their lives and social interactions. Deciding what to consume, what to produce, what is worth paying attention, and even who to relate with and trust can be increasingly taxing endeavors in a world of abundance (Simon, 1971 ). 22 One can only ignore these types of costs associated with consumption and productive use of abundant resources by donning partial equilibrium blinders and assuming away complementarities and interdependencies among abundant and scare resources.

This is a move we refuse to make. To be clear, we do not deny the initial descriptive claim that data, speech, and other intellectual resources as well as means of producing and sharing such resources are increasingly abundant. Rather, we insist on recognizing how scarcity of other complementary resources not only persists but likely increases because of increased abundance of data, speech, and other intellectual resources (C.f. Blevins, 2012 ).

This dynamic consideration raises others. For example, increased demand for and reliance on digital networked technologies to manage these costs of abundance may generate external effects on autonomy and other capabilities essential to human flourishing (Frischmann and Selinger, 2018 ). While it is beyond the scope of this paper to explore fully, we highlight, as a potentially fruitful area of future research, that the types of externalities, and corresponding social demand for governance, may shift from traditional welfare effects (more or less happiness, increased or reduced preference satisfaction) to capability effects (more or less capable, more or less autonomous, more or less rational, more or less creative, etc.). In evaluating the impacts of increased abundance on society, one might ask some basic questions. For example:

• Are people more knowledgeable?
• Are people more capable of accessing and using the knowledge and knowledge-generating technologies in ways that improve their lives and the lives of others?

The abundance of available data and knowledge does not mean that anyone knows everything or really anything at all. Despite wishful thinking of those who embrace the idea of cyborgian mergers of human minds with machines (Clark and Chalmers, 1998 ; Clark, 2003 ), abundant, Internet-accessible resources remain external to the human mind. Thus, to make the point crystal clear: Wikipedia is not part of anyone's mind. It is simply and quite incredibly an easily accessible source of abundant knowledge and means for producing and disseminating knowledge. There is no good reason to presume most people are capable of effectively accessing and using Wikipedia and many other abundant resources. Nor is there a good reason to presume that most people make the effort when they have reason to do so. The exciting fact of abundance too easily obfuscates empirical questions regarding what actual people can do and in fact do.

Some might dismiss our concern by suggesting that whether people avail themselves of abundant resources is simply a question of demand; unfortunately, such a perspective adopts a partial equilibrium, market-based frame and ignores structural conditions, failures in other markets, and non-market considerations. For example, Wikipedia may be accessible and quite useful to schoolchildren completing homework assignments. But technological conditions, such as lack of reliable Internet access, may be a structural barrier, and making effective use of Wikipedia and other abundant knowledge resources available online also may depend on digital literacy and other skills that are not taught or learned equally by everyone. Counterintuitively, the abundance of knowledge resources accessible by the Internet also might encourage forms of outsourcing, overconfidence, and reliance that undermine intellectual development and knowledge acquisition. Frischmann and Selinger ( 2018 ) explore various examples.

The bottom line is that there are many empirical questions that deserve attention if we are to say anything meaningful about how increased abundance affects society. It is important to investigate whether the abundant knowledge available on the Internet is, in fact, socially valuable. Broad claims about democratization or abundance do not provide any insight into quality or value. A more direct line of inquiry would focus on knowledge-based capabilities:

• Are people more or less capable of solving problems?
• Are people more or less creative?
• Are people more or less literate, numerate, empathetic, etc.?
• Have the bounds of bounded rationality been stretched?
• Have people gained or lost common sense?
• Who has gained what intelligence?

We can develop a long list of such questions regarding different types of human intelligence and capabilities (Frischmann and Selinger, 2018 ). Of course, these are generic and in practice entail a set of subsidiary questions that require interdisciplinary study. Nonetheless, we should consider these (and subsidiary) questions before jumping to any conclusions about what abundance means for society. If people are genuinely more capable in meaningful ways in their actual lives, then that would suggest many of the externalities from widespread participation in knowledge production and sharing on the Internet were in fact positive. However, if that is not the case, if people are demonstrably less capable in meaningful ways, then we should consider the possibility of negative externalities, looking to identify and study them, interrogating the mechanisms and causes, and evaluating social demand for governance. Of course, this is no easy task. As we explore below, the scale and scope of externalities is unprecedented and that only complicates the empirical work. The final question deliberately emphasizes distributional concerns in part to counter the “rising tides will lift all boats” style appeal of the abundance hypothesis and in part to prompt consideration of intelligence-based power, which by many (most) accounts in increasingly concentrated.

A related line of inquiry, suggested above, concerns the knowledge systems themselves and potential areas where abundance of some resources create or increase scarcity of others. For example, consider expertise, editorial skills, or other knowledge-related resources associated with quality intermediation (filtering, sorting, content moderation). Dramatic increases in quantity do not necessarily coincide with corresponding increases in quality. In fact, quite the opposite appears to be the case in many, though not all, sectors. Of course, to say this implies that there are accepted means for evaluating quality, which can be a contentious issue when relativism reigns and appeals to authority regularly are challenged. What is the relationship between (i) abundance of knowledge resources and (ii) concentration with respect to the tools, means, and human capabilities for evaluating the quality of such resources? Some might argue that along with abundant knowledge resources have come abundant tools for evaluating quality, ranging from decentralized forms of crowdsourcing to more centralized, platform-based forms of algorithmic content moderation. Others might criticize the availability and quality of these tools, their objective functions (e.g., how they evaluate, what they prioritize), and their impacts upon users and user capabilities (Frischmann and Selinger, 2018 ). It remains unclear whether abundant knowledge democratizes expertise and what that would even mean. Is expertise scarce, concentrated, or abundant? What about trust in experts, expertise, or expert systems? Again, we raise these questions to suggest that this line of inquiry deserves further scholarly attention if we are to evaluate what abundance means for society (Marciano et al., 2020 ).

In the imperfect world where abundance and scarcity vary across resources, people, and contexts, externalities persist, indicate social demand for governance, and should inform comparative analysis and design of governance institutions. The Internet example supports our argument. In our modern digital networked world, externalities are ubiquitous. We hypothesize that there are more externalities than ever in human history and social interdependence is at an all-time high . Recall how the Internet enables nearly instantaneous, incredibly low-cost production and distribution of public goods (data, speech, communications, even software applications). This has led to significant increases in the scale and scope of such goods produced and shared globally. The trillions (or more) of daily acts by ordinary people who produce and share such goods are an important reason for the basic motivating claim about abundance. 23 Yet one can hardly imagine that many actors are aware of, much less appreciate and understand, the full range of effects that follow from their actions. Of course, people generally do understand some of the effects, the more immediate and direct ones as well as some indirect and attenuated ones. But in this context, what they know is necessarily only a fraction. We do not mean to imply anything about the signs or magnitudes of such effects, except that the magnitudes are not likely to be known by the actor. Of course, the signs and magnitudes of effects matter from a social perspective because they add up. Frischmann ( 2012 ) explained this in terms of social demand for the Internet and infrastructural applications-layer platforms. The overwhelming majority of actors may generate small-magnitude spillovers, but the net social impact from widespread production of small-magnitude spillovers can be massive. And at the same time and other extreme, a single actor may produce a “killer app” that generates incredibly large-magnitude spillovers, and the kick is that who will create it and what exactly it will be are impossible to predict ex ante—for both market and government actors. Back in 2012, Frischmann argued in favor of open infrastructures to support the full spectrum of spillovers, contending that the externalities were mostly positive and thus indicative of success rather than failure. 24 Yet 6 years later, Frischmann and Selinger ( 2018 ) raised many of the critical concerns noted in the text above, questioning whether many of the external effects presumed to be positive were either negative or positive but accompanied by other complementary effects that were negative. These views highlight the persistence of externalities and the evolving social demand for governance.

Beyond knowledge about third-party effects, another obstacle to internalization in the digital networked world, and thus reason to believe that there are more externalities than ever before, is the lack of mutuality online . The Internet affords people around the world with the capacity to interact with a much larger number of weak ties and strangers than ever before in human history. Again, such interactions always involve the generation and exchange of public goods. While there is incredible variance in how people interact online and the degree to which such interactions generate externalities, our claim is that both genuine and as if mutuality are often absent, especially among strangers. While genuine mutuality would be difficult to imagine for strangers on the Internet, as if mutuality is possible with appropriate governance institutions in place, as demonstrated by some online communities and platforms that effectively govern shared resources and construct sustainable commons. In our view, widespread and substantial externalities among strangers online presents a strong indication of social demand for governance; design of appropriate governance institutions should account for both the knowledge and mutuality conditions necessary for internalization.

4. Conclusion

Motivated by the abundance hypothesis, this article revisited the economic phenomena of externalities. In the real, necessarily imperfect world where abundance and scarcity vary across resources, people, and contexts, externalities persist, indicate social demand for governance, and inform design and comparative analysis of governance institutions. This article developed the theoretical framework, including a brief intellectual history and notes toward the development of a general theory of externalities. It then explored a series of theoretical and empirical questions that challenge the abundance hypothesis.

Data availability statement

Author contributions.

Both authors contributed to the article and approved the submitted version.

1 This remark resonates with the critique on the contractual and bilateral view on externality adopted by most of the law and economics analysis (Arruñada, 2017 ).

2 We wrote this article for an interdisciplinary audience and conference, Scarcity, Regulation, and the Abundance Society , hosted at Stanford Law School on April 22–23, 2022, and organized by Professors Mark Lemley and Deven Desai.

3 We recognize that some scholars find asking questions without also providing answers to be unscholarly or an insufficient contribution to knowledge. Our view, however, is that identifying gaps in knowledge and the series of questions that should help fill those gaps is an important contribution too often overlooked or dismissed by those who prefer asking and answering conveniently simple questions.

4 We purposely excluded non-human species as well as human-built tools, such as corporations and software, from this definition.

5 Market actors regularly generate externalities when making product and pricing decisions that affect other competitors. Economists have debated whether these effects are really externalities, whether the definition should be adjusted to exclude such effects, and whether a distinction should be drawn between technological and pecuniary externalities. See, e.g., Whitcomb ( 1972 , p. 6) (equating externalities with technological externalities); Posner ( 2003 , p. 7) (defining pecuniary externalities); Duffy ( 2005 , p. 1081–85) (collecting sources and insisting that only technological externalities matter). Frischmann and Lemley ( 2007 , p. 262–64) explain the technological/pecuniary distinction and why it does not hold “once we are willing to entertain the idea that the allocation of rights and thus wealth may have dynamic external effects.” We pick up on some of those arguments below. But the point here is simply to note that the basic definition in the text captures the general phenomenon, yet as the following paragraphs suggest, there remains confusion and disagreement about the relevance of different types of externalities to economic analysis.

6 C.f. e.g., Kim ( 2012 ) shows why travelers on long distance bus travel (Greyhound Line buses) prefer silence as a strategy of disengagement from unknown others. But, of course, this does not apply always and everywhere.

7 Of course, in such an explanation, property rights and transaction costs remain relevant factors that can influence and be influenced by social norms and cultural attitudes.

8 We say more about those additional details below.

9 While Coase ( 1960 ) did not explicitly use the term “property rights,” he referred repeatedly to legal rights concerning property.

10 Coase critiqued the notion that polluter A causes homeowner B to suffer a negative pollution externality and viewed the harm realized by B as jointly produced by both A and B because they engage in interdependent activities—manufacturing and homeownership (Coase, 1960 ; Buchanan and Stubblebine, 1962 , p. 381–82; Cornes and Sandler, 1996 , p. 79–80, 86; De Meza, 1998 , p. 273–74).

11 Pareto superiority is the condition that determines voluntary exchange and makes markets useful. Otherwise, parties have no interest in using the market.

12 Demsetz ( 2011 , p. 655) later explained “[w]hereas Coase's work examined the consequences that followed from an existing private-ownership system, I sought to explain why such a system would come into existence.”

13 As an anonymous reviewer noted, efficient bargaining in this scenario depends not only on zero transaction costs but also on an equal division of wealth.

14 As we explore in more detail below, evaluating social consequences attributable to increased abundance of knowledge and digital networked technologies may require shifting focus from preference satisfaction and other conventional measures of welfare to human capabilities. Cf. Sen ( 1985 , 1999 , 2005 ) (capabilities); Hausman and McPherson ( 2009 ) (preference satisfaction).

15 Many economists have struggled to differentiate externalities from the more general concept of interdependence. We do not. Externalities always, by definition, involve interdependence between two or more people. Such interdependencies are typically mediated through physical and social environments. Not all interdependencies are externalities, however. Most obviously, some interdependencies are fully accounted for by the parties. Perhaps less obvious are those interdependencies that do not involve consequences attributable to human activity.

16 Granovetter ( 1985 ) expressed a similar criticism when discussing the embeddedness of economic activities within society. While a large fraction of human interaction may take place within markets, we cannot capture all the human interaction as a sum of markets.

17 To preempt another potential objection, we acknowledge that while silence may be a public good with positive affordances for some, including writers, it also may be stifling for others who otherwise might produce different public goods, for example, by generating and sharing knowledge by speaking with each other. The complex tradeoffs only strengthen our argument.

18 C.f. Samuelson ( 1954 ); Samuelson ( 1958 , p. 334).

19 Arrow ( 1962 ) made a similar observation in the case of knowledge, which is both an output and an input of inventive processes. Marchese et al. ( 2019 ) tries to give glimpses through a model of endogenous growth.

20 We discuss all three because they surface in discussions of abundance. Lemley ( 2015 ), for example, posits and often refers to the “world without scarcity” (thus, evoking our first conception), but most of his analysis presumes scarcity persists for many resources (such as raw materials) and assumes abundance only for specific sets of knowledge resources (thus, evoking our third conception). Yet there are significant problems with alternating between the first and third conceptions, evoking one but relying on the other, and these problems may be seen through the lens of the use and abuse of the Coase Theorem (our second conception).

21 The idea of overabundance in the sense of oversupplying knowledge may not resonate initially with an economist. Can there be too much of a good thing? What if the supply of such goods generates negative externalities akin to congestion externalities? This would require a congestible (potentially scarce) resource, such as conventional common pool resources. There are a few obvious candidates, such as attention and time. Other candidates include trust and expertise.

22 “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention.” (Simon, 1971 , p. 40–42).

23 We focus on ordinary users to make a point. Of course, we can extend the analysis to the incredibly wide range of professional, commercial, political, educational, governmental, scientific, and other organizational or institutional actors who also produce and distribute these types of public goods and only internalize a fraction of the externalities they generate.

24 The argument is an applied version of the argument we made earlier about setting aside optimality conditions and instead focusing on how to improve market, government, and other social systems for the bulk of inframarginal investments.

Conflict of interest

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

Publisher's note

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Negative externalities in the sharing economy: sources, paths and recommendations

International Journal of Crowd Science

ISSN : 2398-7294

Article publication date: 13 November 2018

Issue publication date: 29 November 2018

This paper aims to clarify the complex path of negative externalities in the sharing economy and proposes corresponding policy recommendations.

Design/methodology/approach

This paper aims to establish an analytical framework for the negative externalities of the sharing economy and to extract the main factors that produce negative externalities, and then, through qualitative comparative analysis method find out how these factors interact to form a negative externality.

Negative externalities in the sharing economy come from the joint effect of the sharing degree of the product or service and constraint mechanism, and the current main modes of the shared economy increase the possibility of negative externalities.

Originality/value

The paper proposes a complex path resulting from negative externalities in the shared economy.

• Negative externalities
• Qualitative comparative analysis
• Sharing economy

Jing, W. and Sun, B. (2018), "Negative externalities in the sharing economy: sources, paths and recommendations", International Journal of Crowd Science , Vol. 2 No. 2, pp. 149-163. https://doi.org/10.1108/IJCS-08-2018-0016

Emerald Publishing Limited

Copyright © 2018, Wenjun Jing and Baowen Sun.

Published in International Journal of Crowd Science . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The global success of business models represented by Airbnb and Uber have opened various resources for sharing, influencing the rise of sharing economy. The sharing economy has been generated under the background of overcapacity, referring to a new model in which internet companies use algorithms and data to mobilize redundant resources in the market, solve asymmetric market information and improve transaction efficiency. Globally, tens of thousands of various types of sharing economy enterprises exist, affecting people’s lives and consumption patterns in various fields. The 2017 Sharing Economy Development Report released by the China Electronic Commerce Research Center pointed out that in 2017, China’s sharing economy was more than 5tn, with a growth rate of more than 40 per cent since 2016. The sharing economy has played an important role in improving the allocation efficiency and user value of social resources. However, it has also brought some negative effects, such as shared bikes occupying sidewalks, drivers violating regulations, price increases and tenants destroying homes in short-term rentals.

Recently, countries around the world have begun to actively respond to the negative effects of the sharing economy. For example, in terms of sharing trips, Singapore and other countries recognize the legitimacy of vehicles such as Uber, but under strict regulations; France and others investigate online car rental services as illegal businesses; in California and Colorado in the USA and Washington, DC the legalization of online car rental services has already been achieved. Many studies have begun to focus on governance issues regarding the sharing economy and have proposed various suggested governance methods such as multiple governance (People’s Forum questionnaire survey center, 2017), collaborative governance ( Tang, 2017 ), mixed supervision ( Tang, 2015 ), response-type regulation ( Peng, 2016 ) and so on. However, traditional governance methods produce difficulties for sharing economy as a new, effective economic form. Main reasons for this include the lack of a clear understanding of the emergence of various problems in the sharing economy and the weak pertinence of proposed governance methods. The relative lag in theoretical research has also caused difficulties for relevant departments in the actual governance of the industry.

The sharing economy situation fits well with the externality theory. Pigou (1962) believes that externality is the inevitable result of market operations. When externalities exist, the allocation of resources cannot reach Pareto optimality. Therefore, identifying sources of negative externality may be effective in determining entry points for governance of the sharing economy. Based on the aforementioned, this article discusses the source of negative externalities in the sharing economy to explore which economic features constitute the source of negative externalities and whether specific patterns exacerbate these negative externalities. We further propose corresponding governance strategy.

2. Literature review

The discussion of externalities has been around for a long time, but the concept is still controversial. Historically, the point of view on externalities can be divided into three major nodes. Currently, the general awareness in the academic world attributes the external economy, which was first proposed by Marshall (Zhang, 2012). The external economy, or the changes in business efficiency caused by changes in industry scale, is considered the predecessor of externality. Originally, however, the concept of externality was first put forward by Pigou (1912). In his book, Welfare Economics , the externalities are explained by analyzing the deviation between the marginal private net output value and the marginal social net output value. Pigou believes that externality is actually the inconsistency between marginal private cost and marginal social cost and marginal private income and marginal social income. However, the concept of externality discussed by Coase in the later period was different from Pigou. He proposed that externality is the direct influence of economic activities of the actor on other economic entities in his paper “Problem of Social Cost.” The negative externalities of the sharing economy discussed in this paper are conceptually closer to Coase’s research.

The existence of externalities has been widely accepted; numerous scholars, in addition to discussing the mechanisms, classifications, etc., have also discussed external phenomena of externalities and their appearances ( Scitovsky, 1954 ; Bator, 1958 ). Externalities have been studied in the banking sector (Su, 2000; Zhang, 2016 ), insurance ( Zhou, 2014 ), coal mining ( Liu, 2014 ) and other specific industries. Despite the in-depth exploration of externality, it is still a vague concept. In the narrative definition, externality is generally defined as the influence the implementer has on other people in a particular economic activity. For example, the New Palgrave Dictionary of Economics defines external economies as an effect of a producer’s output or input on the nonpayment of another producer. Buchanan and Stubblebine (1962) gave a mathematical representation of externalities: the so-called externality is the independent variable of the welfare function of an economic entity, producing a function that shows if an economic welfare task is affected by other factors not controlled by oneself, there is an externality. Similar definitions include Ping (2006) and Xu (2006). In this type of definition, the subject is clear, but economic activity is ambiguous. The influence of the implementer on other people is an overly broad term. Therefore, key content in the definition is unclear therefore confusing the sources, process of influence and extensions of externalities.

Although the aforementioned representative studies by Marshall, Pigou, Coase and others have greatly improved people’s understanding of externality issues, they actually provide inconsistent discussions of externalities. Marshall’s externality refers to the impact from activities of other economies, with the typical example the tragedy of the commons. Pigou’s externalities refer to the influence of actors on society and the natural environment, such as global warming and intergenerational equity in sustainable development theory. Coase’s externality advocates the influence of actors on direct participants, such as the impact of factory sewage on fish farms. The aforementioned studies were also conducted at different levels in terms of extension, which has exacerbated the ambiguity of the concept of externality. Many economists, such as Tibor Scitovsky, Zhang, Yang and others, believe that the definition of externality is a rather vague concept. Hu Shiqing and Wu Jiapei (2011) summed up the research on externalities by Marshall, Pigou, Coase and other scholars and provided two essential features of externalities: that the affected party has “non-participatory decision-making” and “has lacked an effective feedback mechanism.” Based on this, a more comprehensive definition can be given: in a particular economic activity, externalities are the effects on parties that have not participated in the decision and lack an effective feedback mechanism to compensate. The externality is generated and exists in this way.

Despite the unclear definition of externality, current research on the externalities of an industry often does not consider the ambiguity of the concept. Research situations on the topic more common in traditional industries and have provided some inspiring conclusions. However, it is undeniable that traditional industries are relatively simple and their economic activities are relatively stable. For emerging economies which are sharing economies, new resource allocation methods, transaction circulation modes, diversified needs and business types may be impossible under vague framework for comprehensive and accurate analysis. It is too easy to catch only a few details and conduct incomplete study under the current environment.

The ambiguity of the externality concept creates difficulties for quantitative analysis of the presence and origin of externalities. Existing quantitative or empirical studies have focused on the FDI industry and often examine externalities through the spillover effect of technology or knowledge (Aitken and Harrison, 1999; Wen and Liang, 2011 ). Another type of study is the discussion of the endogenous nature of externalities ( Mitchell and Skrzypacz, 2006 ). The empirical approach of the former is generally to reflect the marginal effects of variables through regression coefficients; the latter is usually derived through mathematical model derivation. However, the aforementioned research methods cannot explain the source of externalities well. First, the basic idea in the regression method is weak in explaining the cause and effect relationship. Second, the complexity of the concept of externalities makes it difficult to abstract a reasonable mathematical expression. Finally, the sharing economy studied in this paper is a new economic form.

At present, there are no mature technical means or models that can be used to describe the external effects of an activity, and it is impossible to explore the sources of externalities. Based on the three aforementioned points, we need to adopt a new method to explore the sources of negative externality in the sharing economy. Qualitative comparative analysis (QCA) provides useful ideas for this study. This paper believes that the QCA method is more suitable for the study than the traditional statistical methods, with the main reason that QCA has a significant advantage in explaining the causality.

At present, research on the sharing economy has focused primarily on the drivers ( Shirky, 2009 ), impact assessments (European Parliament, 2015; Greenwood and Wattal, 2015 ), business model characteristics ( Henten and Windekilde, 2015 ), effective management ( Zhang, 2016 ; Chen, 2016 ; Liu and Wang, 2016 ) and so on. However, mature studies on externalities of the sharing economy are rare. At present, there are no professional papers or authoritative reports concerning the externalities of the sharing economy, with references to the negative externalities of the sharing economy often found in Web news reports. The reasons for the separation of realistic and theoretical research may include that the sharing economy has appeared recently, and its understanding is still deepening. Also, as aforementioned, externality is a relatively vague concept, and therefore, although we can recognize its existence, it is difficult to analyze with mature methods. The over-innovation of research objects has also made it difficult to find effective research methods. On the other hand, there has been an endless stream of research on the sharing of economic supervision and governance. In fact, these studies have illustrated that the sharing economy has brought a series of benefits to society, while simultaneously producing certain negative consequences. A large part of these consequences can be summarized and interpreted with negative externalities. However, the existing research neglects analysis of problem causes and discusses the governance methods directly, lacking innovative conclusions.

Therefore, in the discussion of the negative externalities of the sharing economy, we first need to establish a normative framework to study the scope of externalities, the production mechanisms and the extension of performance. The aforementioned definitions have not been widely recognized, but inspiration can be drawn from these studies. First, externalities are related to human behavior; Marshall, Pigou and Coase all involved this aspect in the definition. Therefore, the behavior of the economic subject can be used as the starting point of externality study. Second, economic entities can be divided rationally as the generator and the receiver of externality. This division can better determine whether the externality exists. Finally, on the basis of this judgment, to explain the causes or mechanisms of externalities, it is possible to start by distinguishing the behaviors that generate externalities and examining what factors have caused these behaviors.

3. Analysis framework

Positive externalities in the sharing economy can be clearly defined and reflected in the reduction of transaction costs and improvement of transaction efficiency. This is determined by the original intention of the sharing economy. Negative externalities, however, have a variety of expressions, for example, man-made destruction of shared resources (such as bicycles and rooms), indiscriminate use of shared bicycles affecting traffic operation and security problems arising from online car rental. The aforementioned issues have different performances and involve different levels of interpretation. Man-made destruction of shared resources involves the issue of public goods, the illegal parking of shared bicycles involves the issue of property rights separation and the security problem caused by online car rentals is mainly from the assumption of reasonable persons who act as profitable agents.

It can be seen that the sources of negative externalities in the sharing economy are mainly related to participant behaviors. Immoral behavior means that “self-interested individuals are affected by certain factors and violate general principles such as honesty and reliability because the environment allows him to do so with impunity” ( Pang, 2004 ). Therefore, the focus of research on the negative externalities of the sharing economy lies in the analysis of conditions for immoral behaviors. On the other hand, the Mandeville Paradox points out that private evil can be transformed into public interest. This can reveal that some deterrence is not necessarily the source of diseconomy; on the contrary, it is possible that strong self-interest promotes public interest. This kind of thinking is also reflected in the first theorem of welfare economics. That is, if companies pursue interests and every individual pursues his/her own maximization of benefits, market resource allocation can reach Pareto optimality.

Therefore, in the analysis of negative externalities, it is not possible to consider only the subjective behavior of the sharing economy as an independent variable, but other variables must also be included. In addition to the costs and benefits of self-interested behavior, some new issues arising from the sharing economic business model should also be considered, including property rights separation, public goods issues and information asymmetry.

4. Research methods and processes

4.1 new idea: qualitative comparative analysis, 4.1.1 the origin and application of qualitative comparative analysis..

QCA is an analysis method for small and medium-sized samples of case studies, first proposed in social science research in the 1980s by Charles Ragin. This method is based on case studies and can systematically analyze small data samples. It combines the strengths of both qualitative and quantitative analysis methods, treating each case of the study as a whole and analyzing the interpretation conditions and configurations in the case. In the early use of the method, a clear set qualitative comparative analysis (csQCA) technique was mainly used. However, this technique can only deal with binary variables, that is, the causality condition of the analysis, and the result value must be calibrated to 0 or 1, facilitating the loss of variable information and production of contradictory configurations and increasing the difficulty and challenge of analysis ( Cronqvist and Berg-Schlosser, 2009 ). To overcome this defect, Ragin (2008) proposed the fuzzy set qualitative comparative analysis (fsQCA) technology, adopting a fuzzy set to indicate the degree of occurrence of results and the interpretation conditions. In principle, any value between 0 and 1 can be assigned. The loss of information in the process of data transformation can be avoided, and the actual case situation can be more accurately reflected. Therefore, this paper chose the fuzzy set QCA method.

4.1.2 The principle of qualitative comparative analysis.

The core logic of QCA is derived from set theory. If a problem or phenomenon to be studied is viewed as a set, the preconditions causing the phenomenon are subsets. For example, when analyzing the source of negative externalities in the sharing economy, “negative externalities in the sharing economy” is a set, and the subsets of preexisting conditions can be “gains from immoral behavior,” “products or services with the nature of public goods,” “limited industry constraints” and so on. On the technical level, the QCA method uses a Boolean algebra algorithm to find certain affiliation relationships that exist among the collections through a certain number of multi-case comparisons. As there are equivalent multiple paths or solutions in research (Ragin, 2000, 2014; Rihoux and Ragin, 2009 ; Fiss, 2011), the QCA method may explore multiple relationships between combinations of condition variables.

Compared with the general regression method, the QCA method is more helpful in identifying the multiple concurrency causes of the results. The configuration theory and model (three interactions) have higher explanatory power than the contingency theory and model (binomial interaction), which has already formed consensus in the academic community (Dess et al. , 1997). Traditional contingency methods only analyze the relationship between a certain independent variable and a dependent variable. The QCA method with the configuration perspective can handle the multi-dimensional and multi-causal conditions of interdependence, configuration equivalency, causal asymmetry and other causal complexity issues ( Fiss, 2007 ). Differences between using the general regression method and the QCA method to study “the source of negative externalities in the sharing economy” can be more intuitively represented by Figures 1 and 2 , respectively.

From Figures 1 and 2, we can see that the general regression analysis focuses on the marginal net effect of the independent variable on the dependent variable; therefore, the complex causal relationship between independent variables cannot be explained. The QCA method adopts a configuration perspective and reflects how the condition variables (corresponding to the independent variables) cause the result variables (dependent variables) to occur in different combinations.

The calculation principle of QCA is based on Boolean algebra. A variable of 1 indicates that a certain condition occurs, and 0 indicates that a certain condition does not occur. A value of 0 is represented by “∼”. The logical operator “*” means “and,” and “+” means “or.” The symbols “=” or “→” mean “cause.” For example: “A*B + C*∼D → Y” indicates that conditions where A and B coexist, or C occurs and D does not occur, may cause Y to occur. Measuring the reliability of QCA results can be performed through two indicators: coverage and consistency, calculated as follows: (1) C o v e r a g e ( X i ≤ Y i ) = ∑ ( min ⁡ ( X i , Y i ) ) / ∑ Y i (2) C o n s i s t e n c y ( X i ≤ Y i ) = ∑ ( min ⁡ ( X i , Y i ) ) / ∑ X i

In the formula, X i represents a set of certain condition variable combinations, and Y i represents a case result set. The coverage rate examines the extent to which X i can form the necessary conditions for Y i ; consistency examines how much X i can deduce the outcome of the Y i set.

4.1.3 General steps for qualitative comparative analysis.

QCA begins with the selection of cases based on research questions. This is a process of constant interaction between theory and experience. As QCA analyzes conditional combinations rather than cases, conditional variables need to be determined according to various strategies and corresponding theories. The purpose is to derive the data combination of condition and result variables then build a truth table to reflect all combinations of condition variables and result variables. In this process, contradictory conditional combinations may be encountered, requiring calibration. Finally, the truth tables are simplified according to Boolean algebra to obtain various combinations of conditions that lead to the occurrence of result variables.

4.2 Case selection

Case selection is the basis for OCA. As the sharing economy is a new kind of economic model, internet news reports can fully reflect its characteristics, and research results may be in touch with reality. This article searched keyword “sharing economy” to extract typical negative cases of the sharing economy that have occurred in the past two years. To ensure research quality, cases were screened in two rounds. In the first round, cases that met the following conditions were selected: cases listed on mainstream media and cases described in detail to ensure that relevant information could be obtained. In the first round, 39 cases were selected. In the second round of screening, cases with the same event attributes were removed. For example, only one case of privatization of shared bicycles was retained. Finally, 15 cases were coded (refer Table I for details).

4.3 Truth table construction

The construction of a truth table is a key step in QCA. Based on the analysis framework proposed in the previous section, this article focuses on the condition variables that result from negative externalities in the shared economy. This includes the loss of gains, expected compensation, convenience of defeat and degree of public goods. There are six aspects to the degree of separation of property rights and the restraint mechanism of failure. The outcome variable chosen is the severity of negative externalities. Based on the aforementioned variables, a truth table was formed. The specific assignment method is as follows.

4.3.1 The severity of negative externalities (R1).

The severity of negative externalities was the outcome variable, using a three-value assignment scheme with 0 as the lowest severity, 0.5 moderately severe and 1 the highest. Assignment rules were based on the actual damage caused in the case. Inflicting harm to others, causing serious loss of property or spirituality to others, or repeatedly behaving in a highly unpromising manner resulting in strong social adverse effects, etc., were assigned a value of 1. The variable was set to 0 for scenarios that did not directly harm others. The remaining cases were assigned 0.5. For example, Case 9 mentioned “Didi’s driver escaping after hitting someone” causing harm to the life of others and was assigned the value of 1. Case 7: sharing bicycles near Beijing East Station piled up into hills, a group of irregular behaviors, was assign 0. It is noteworthy that the severity of negative externalities in some cases did not have a distinct degree of differentiation, so the authors discussed in detail during the assignment process until a consensus was reached. Subsequent variables that involved segmented assignments follow the same approach.

4.3.2 Immoral behavioral benefits (Benefit – C1).

Immoral behavioral benefits are an important part of the traditional analysis perspective of cost – benefit analysis. The assignment is more complicated because some benefits are material gains, while some are psychological satisfaction. To better characterize these differences, a four-value valuation scheme was used for the return factor, which was assigned to 0, 0.33, 0.67 and 1 based on low or high returns. The assignment rules were divided according to the physical or psychological gains of the immoral subject, for example, the behavior of obtaining economic benefits directly by replacing the shared bicycle’s QR code was assigned a value of 1 (Case 15).

4.3.3 Expected compensation (Compensation – C2).

Immoral behavioral benefit and expected compensation serve as important components of traditional analytical perspectives. Expected compensation mainly reflects the cost aspect. According to the expected level of compensation, values were assigned 0, 0.5 or 1, from low to high. The assignment rules referred to the value of damage to physical objects and the behavioral characteristics of the participants. For example, Didi’s driver made a collision and escaped the scene (Case 9); “escape” was used to determine that the driver’s expected compensation for the event was high, so the value was assigned 1. In Case 8, a woman was called to the crematorium by the drip driver every day for half a month only because of bad feedback to the driver. The behavior in this case exhibits a continuous process, indicating that the participant’s expected compensation for the matter was extremely low, so the value was set to 0. Other cases, such as the destruction of shared bicycles or housing, were set to 0.5.

4.3.4 Convenience of immoral behavior (Convenience – C3).

The foregoing reveals that the sources of negative externalities are largely related to behavior. Therefore, the convenience of immoral behavior reflects the possibility of negative externalities. According to the degree of convenience, values of 0, 0.5 and 1 were assigned, from low to high. For example, parking shared bicycles randomly as a habit is highly convenient, and the assigned value was 1; it is less convenient to throw a shared bicycle into the river or burn a shared bicycle, so the assigned value was 0.

4.3.5 Nature of public goods (Public – C4).

Characteristics of public goods are a prominent aspect of products and services in the sharing economy. Public goods refer to goods and services provided by the public sector to meet the public needs of society. Public goods have the characteristics of indivisibility, non-competitiveness and non-excludability. Obviously, these characteristics in sharing bicycles is significantly higher than cars, such as rental services represented by Didi, and homes, such as rental services represented by Airbnb. Therefore, cases involving a shared bicycle were assigned a value of 1, and the remaining cases were assigned a value 0.

4.3.6 Separation of property rights (Use or possess – C5).

The separation of property rights is an important feature of the sharing economy. The method used to measure the separation of property rights in this paper was whether in each case the participating entity had the right to use the product or service. In cases when there was only the right to use, we believe that the degree of separation of property rights is relatively high. Cases with this condition were assigned to a value of 1 and the rest of the cases 0.

4.3.7 Constraint mechanism (Constraism – C6).

To restrict the negative externalities that arise from sharing of products or services and reduce the unnecessary costs to enterprises or society, entities in the sharing economy have established a certain constraint mechanism. The two most common mechanisms are the deposit mode and the evaluation mode. This article sets the deposit mode to 1 and the comment mode to 0.

From this, we obtained the truth table for this article, shown in Table II .

5. Result analysis

5.1 single factor discussion.

First, we examined the relationship between single conditional variables and negative externalities. The results are shown in Table III . None of the six conditional variables satisfied the conditions shown in equation (1) from the point of view of consistency. From the perspective of coverage, only the variable of expected compensation met the conditions of equation (2) . Therefore, a single conditional variable cannot explain the formation of negative externalities in the sharing economy. The results in Table III also indirectly illustrate the necessity of QCA.

5.2 Traditional perspective discussion

Of the six conditional variables selected in this paper, the traditional perspective of cost – benefit analysis identified three which form negative externalities – immoral behavioral benefits, expected costs and convenience of immoral behavior. The purpose of the traditional QCA method is to identify the role of sharing economic characteristics in the negative externalities. Using fs/QCA2.0 software, case codes with continuity of less than 0.8 were deleted, and standard analysis was selected. The output results are shown in Table IV .

In Table IV , coverage (or raw coverage) represents the effect of each factor in each condition combination on the results and is generally not analyzed. The value of unique coverage indicates which combinations are more able to achieve the result, and consistency reflects the number of cases that the condition combination can explain. On the whole, the consistency was higher, exceeding 0.97, which meets the requirements. The overall coverage was low, which may be constrained by the sample size.

Traditional Path 1 : Source of negative externalities in the sharing economy = (high immoral behavioral benefits) * (low expected compensation) * (inconvenience of immoral behavior); and

Traditional Path 2 : Source of negative externalities in the sharing economy = (low immoral behavioral benefits) * (high expected compensation) * (convenience of immoral behavior).

The two aforementioned paths are in line with experience. That is to say, when immoral behavior has high benefits and low cost, negative externalities will still be generated, even with lack of convenience. On the other hand, if it is convenient to exercise the immoral behavior, then even if the gap between the benefits and costs is not particularly large, negative externalities are also likely to arise. From the size of the raw coverage, it can be seen that the Traditional Path 1 is more likely to occur. However, the aforementioned results do not portray the characteristics of the sharing economy. The question of whether a special model such as the sharing economy has exacerbated negative externalities cannot be explained well. Therefore, the path to negative externalities in the sharing economy should be studied further.

5.3 Comprehensive discussion

In the same way, the aforementioned six conditional variables were put into the same analysis framework. The output is shown in Table V .

Path 1 : Source of negative externalities in the sharing economy = (low immoral behavioral benefits) * (low expected compensation) * (convenience of immoral behavior) * (low level of public goods) * (low level of separation of property rights) * (evaluation constraints);

Path 2 : Source of negative externalities in the sharing economy = (low immoral behavioral benefits) * (high expected compensation) * (convenience of immoral behavior) * (low level of public goods) * (high level of separation of property rights) * (evaluation constraints); and

Path 3 : (high immoral behavioral benefits) * (low expected compensation) * (inconvenience of immoral behavior) * (high level of public goods) * (high level of separation of property rights) * (deposit constraints).

Of the aforementioned three paths, Paths 2 and 3 complement the traditional path under the sharing economy environment. Among them, Path 2 shows that when there is high convenience in immoral behavior, higher degrees of separation of property rights and impacts of evaluation constraints and lower levels of public goods will reduce negative externalities. On the other hand, Path 3 reveals another economic model that promotes negative externalities: high level of public nature of goods, high level of separation of property rights and the deposit constraint mechanism, similar to shared bicycles.

In addition, Paths 1 and 3 illustrate that the negative externality of the sharing economy is related to specific business models. Specifically, Path 1 illustrates that negative externalities in the sharing economy can result from the following circumstances: a low level of public goods, property rights that are not completely separated and the evaluation constraint mechanism. These characteristics are in line with the characteristics of online car rentals and other short-term rental services. In this kind of environment, the levels of benefits and costs of immoral behaviors have the same effect on negative externalities. The lower the two, the more likely negative externalities will arise. In this environment, the Traditional Path 1 is more likely to form negative externalities. Comparing Path 1 and Path 3 can further determine that increases in the degree of sharing of products or services (high level of public goods and high level of separation of property rights) and more stringent constraint models (deposit constraint) contribute to negative externalities in the sharing economy. This actually forms a kind of paradox. From an empirical point of view, the higher the degree of sharing, the more stringent the constraint mechanisms are generally required to be. However, research in this paper shows that it is precisely this kind of strict constraint mechanism that causes higher degrees of sharing to form negative externality.

6. Recommendations

The aforementioned conclusions indicate that the common business models in the current sharing economy cannot spontaneously avoid negative externalities but instead have the potential to promote negative externalities. Therefore, the corresponding governance model is needed to ensure a sound development of the sharing economy. This paper proposes the following recommendations.

First, highlight the platform corporate responsibility. The sharing economy makes economic activities not only show the characteristics of happening at any time and everywhere. Moreover, the patterns, groups and fields in which they occur are also expanding. In this case, it is impossible to achieve effective governance of the sharing economy through a top-down perfect system. Therefore, it is necessary for the platform enterprises to assume certain social responsibilities and supervise the main participants of the sharing economy. The key is to determine the degree of shared services by the platform enterprises to adapt to the corresponding constraint mechanism.

Second, establish a multi-subject governance framework. The sharing economy is a platform for sharing, co-constructing and co-governing in which the whole society participates. Therefore, in addition to the government and enterprises, there should also be public participation. Therefore, it is necessary to establish a multi-governance system including the government, enterprises and public. The multi-governance system needs to clarify the governance boundaries, scientific division of labor and efficient cooperation among the various entities. Guiding the public to participate in governance actively, making it not only a participant in the sharing economy but also a manager of the sharing economy.

Finally, build a multi-level safeguard and constraint mechanism. This part includes two aspects, one is to establish and improve industry norms and laws; the other is to use new technologies such as big data and cloud computing to incorporate individual behavior into the personal credit system and form a new constraint mechanism.

7. Conclusion

The rapid development of the sharing economy has improved the efficiency of resource utilization, contributing to the quality of economic development. However, various undeniable problems have arisen in the operation and development of the sharing economy at home and abroad, with very prominent negative externalities brought by its disorderly development.

Therefore, this article analyzed the source of negative externalities in the sharing economy. Through the QCA method, three sources of negative externalities were obtained. We believe that sharing of products or services and restraint mechanisms work together to create conditions for negative externalities in the sharing economy. Results found that sharing economy modes with less sharing and more relaxed constraint mechanisms, such as online vehicles and network short-term rentals or modes with higher sharing nature and stricter restraint mechanisms, such as shared bikes, both promoted negative externality. Finally, corresponding policy recommendations are proposed for this issue. This paper started with practical problems and proposed how various factors interact and ultimately contribute to the formation of negative externalities in the sharing economy. The conclusion of this paper provides a new path for the healthy development of the sharing economy and avoids its adverse effects. It also provides a theoretical basis for the government governance of the sharing economy.

Schematic diagram of the general regression method

Schematic diagram of QCA[ 1 ]

Truth table

Single factor analysis results

Negative externalities of the sharing economy from the traditional perspective

Formation path of negative externalities in the sharing economy

Figure 2 only shows the result form of the QCA method and does not represent the final results of this study.

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Markets, Market Failure and the Role of Government

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This chapter introduces Pareto efficiency as the key normative concept of welfare economics and describes the conditions under which we expect efficiency or market failure. We pay particular attention to the existence of externalities and show how these can, but do not always, cause markets to fail. Whenever there is market failure it is conceptually possible for government to intervene in order to improve outcomes. Government failure also occurs, however, and so such intervention is not always practical. When considering imperfect alternatives, we must engage in comparative institutional analysis.

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In the standard analysis, the tax is levied on the production of the good itself. As Plott ( 1966 ) points out, the tax should instead be levied on the output of the externality (i.e. the pollution) or on the resource which causes the pollution. This provides the right incentives for firms to change their production process to remove externalities rather than simply reducing production. Since it does not alter the central points we wish to make in this chapter, we will use the simpler case of taxes imposed on the output of a good.

Inframarginal externalities can in some cases be inefficient in the sense that net benefits would be increased by moving from equilibrium to a much smaller or greater level of output, even though marginal changes would cause inefficiency (Buchanan and Stubblebine 1962 : 374).

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Dowding, K., Taylor, B.R. (2020). Markets, Market Failure and the Role of Government. In: Economic Perspectives on Government. Foundations of Government and Public Administration. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-19707-0_2

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What Is an Externality?

Understanding externalities, externality solutions.

• Real-World Examples
• Externality FAQs

The Bottom Line

Externality: what it means in economics, with positive and negative examples.

Peter Westfall is a distinguished professor of information systems and quantitative sciences at Texas Tech University. He specializes in using statistics in investing, technical analysis, and trading.

Katrina Ávila Munichiello is an experienced editor, writer, fact-checker, and proofreader with more than fourteen years of experience working with print and online publications.

An externality is a cost or benefit that is caused by one party but financially incurred or received by another. Externalities can be negative or positive. A negative externality is the indirect imposition of a cost by one party onto another. A positive externality, on the other hand, is when one party receives an indirect benefit as a result of actions taken by another.

Externalities can stem from either the production or consumption of a good or service. The costs and benefits can be both private—to an individual or an organization—or social, meaning it can affect society as a whole.

Key Takeaways

• An externality is an event that occurs as a byproduct of another event occurring.
• An externality can be good or bad, often noted as a positive externality or negative externality.
• An externality can also be generated when something is made (i.e. a production externality) or used (i.e. a consumption externality).
• Pollution caused by commuting to work or a chemical spill caused by improperly stored waste are examples of externalities.
• Governments and companies can rectify externalities by financial and social measures.

Investopedia / Madelyn Goodnight

Externalities occur in an economy when the production or consumption of a specific good or service impacts a third party that is not directly related to the production or consumption of that good or service.

Almost all externalities are considered to be technical externalities. Technical externalities have an impact on the consumption and production opportunities of unrelated third parties, but the price of consumption does not include the externalities. This exclusion creates a gap between the gain or loss of private individuals and the aggregate gain or loss of society as a whole.

The action of an individual or organization often results in positive private gains but detracts from the overall economy. Many economists consider technical externalities to be market deficiencies, and this is the reason people advocate for government intervention to curb negative externalities through taxation and regulation.

Externalities were once the responsibility of local governments and those affected by them. So, for instance, municipalities were responsible for paying for the effects of pollution from a factory in the area while the residents were responsible for their healthcare costs as a result of the pollution. After the late 1990s, governments enacted legislation imposing the cost of externalities on the producer.

Many corporations pass the cost of externalities on to the consumer by making their goods and services more expensive.

Types of Externalities

Externalities can be broken into two different categories. First, externalities can be measured as good or bad as the side effects may enhance or be detrimental to an external party. These are referred to as positive or negative externalities. Second, externalities can be defined by how they are created. Most often, these are defined as a production or consumption externality.

Negative Externalities

Most externalities are negative. Pollution is a well-known negative externality. A corporation may decide to cut costs and increase profits by implementing new operations that are more harmful to the environment. The corporation realizes costs in the form of expanding operations but also generates returns that are higher than the costs.

However, the externality also increases the aggregate cost to the economy and society making it a negative externality. Externalities are negative when the social costs outweigh the private costs .

Positive Externalities

Some externalities are positive. Positive externalities occur when there is a positive gain on both the private level and social level. Research and development (R&D) conducted by a company can be a positive externality. R&D increases the private profits of a company but also has the added benefit of increasing the general level of knowledge within a society.

Similarly, the emphasis on education is also a positive externality. Investment in education leads to a smarter and more intelligent workforce. Companies benefit from hiring employees who are educated because they are knowledgeable. This benefits employers because a better-educated workforce requires less investment in employee training and development costs.

Production Externalities

A production externality is an instance where an industrial operation has a side effect. This is often the type of externality used as example, as it is easy to envision an environmental catastrophe caused by improperly stored chemicals by a chemical company. Because of how the company produced its goods or protected its waste, an externality occurred.

Consumption Externalities

Externalities may also occur based on when or how a consumer base utilizes resources. Consider the example of how you get to work. Those who choose to drive are creating a pollution externality by driving their own car. Those who choose to take public transit or walk are not causing the same externality. Instead of a side effect occurring because something is being produced, an externality is caused because of an item being consumed.

These four types of externalities above are often combined to define a single externality. For example, an externality may be a positive production, negative production, positive consumption, or negative consumption externality.

There are solutions that exist to overcome the negative effects of externalities. These can include those from both the public and private sectors .

Taxes are one solution to overcoming externalities. To help reduce the negative effects of certain externalities such as pollution, governments can impose a tax on the goods causing the externalities. The tax, called a Pigovian tax —named after economist Arthur C. Pigou—is considered to be equal to the value of the negative externality.

This tax is meant to discourage activities that impose a net cost to an unrelated third party. That means that the imposition of this type of tax will reduce the market outcome of the externality to an amount that is considered efficient.

Subsidies can also overcome negative externalities by encouraging the consumption of a positive externality. One example would be to subsidize orchards that plant fruit trees to provide positive externalities to beekeepers.

This nudge has the potential to influence behavioral economics, as additional incentives one way or another way dictate the choices that are made. The subsidy is often placed on an opposing item to detract from a specific activity as well. For example, government incentives to upgrade to more energy-efficient renovations subtly discourages consumers against options with more externalities.

Other Government Regulation

Governments can also implement regulations to offset the effects of externalities. Regulation is considered the most common solution. The public often turns to governments to pass and enact legislation and regulation to curb the negative effects of externalities. Several examples include environmental regulations or health-related legislation.

The primary issue with government regulation of externalities is the need for consistent and reliable information to track the externality is being managed or overcome. Consider regulation against pollution. The government put forth resources to ensure that the legislation put in place is actually being followed, including holding bad actors accountable for not properly addressing their externality.

Real-World Examples of Externalities

Many countries around the world enact carbon credits that may be purchased to offset emissions. These carbon credit prices are market-based that may often fluctuate in cost depending on the demand of these credits to other market participants.

One program within the United States is the Regional Greenhouse Gas Initiative (RGGI). The RGGI is made up of 12 states: California and 11 Northeast states. RGGI is a mandatory cap-and-trade program that limits carbon dioxide emissions from the power sector.

Different agencies are imposed a cap on externalities, though they can trade resources to change what their cap is. Agencies that struggle managing their externality (i.e. pollution) may need to purchase additional credits to have their cap increased. Other agencies that conquer their externality may sell part of their cap space to recover capital likely used to overcome their externalities.

How Do Externalities Affect the Economy?

Externalities may positively or negatively affect the economy, although it is usually the latter. Externalities create situations where public policy or government intervention is needed to detract resources from one area to address the cost or exposure of another. Consider the example of an oil spill; instead of those funds going to support innovation, public programs, or economic development, resources may be inefficiently put towards fixing negative externalities.

What Is the Most Common Type of Externality?

Most externalities are negative, as the production process often entails byproducts, waste, and other consequential outcomes that do not have further benefits. This may be pollution, garbage, or negative implications for worker health. Many externalities are also related to the environment, as the mechanical nature of manufacturing and product distribution has many detrimental impacts on the environment.

How Can You Identify an Externality?

Companies must be mindful of their entire production process when assessing production externalities. This includes not only implications of the final product but residual impacts of byproducts, disposal of items not used, and how antiquated equipment is handled. This also includes projecting outcomes of items yet to occur, such as waste yet to be properly disposed of.

Consumers can identify consumption externalities by being mindful of the inputs and outputs that go beyond what they are attempting to achieve. Consider an example of an individual consuming alcohol. A consumer must be mindful that excessive drinking may lead to noise pollution, an unsafe environment, or adverse health effects.

How Do Economists Measure Externalities?

Economists use two measures to evaluate an externality. First, economists use a cost-of-damages approach to evaluate what the expense would be to rectify the externality. As we may be seeing with greenhouse gas emissions, some externalities may extend beyond the point of repair.

Another method of measuring externalities is the cost of control method. Instead of fixing the externality, economists measure what preemptive and preventative steps can be taken to stop the externality from occurring. Similar to how an actuary assesses a financial value to an event, economists may assign multiple financial measurements to an externality.

An externality is a byproduct of a primary process. This side effect may be good or bad and may be caused by a production process or consumption process. Many externalities relate to the environment due to the nature of company and individual actions, though there are many ways governments, companies, and people can take responsibility to both prevent and rectify externalities.

Center for Climate and Energy Solutions. " Regional Greenhouse Gas Initiative (RGGI) ."

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• Published: 11 April 2024

Trade-offs in the externalities of pig production are not inevitable

• Harriet Bartlett   ORCID: orcid.org/0000-0002-7389-8785 1 , 2 , 3 , 4 ,
• Márcia Zanella 5 ,
• Beatriz Kaori 5 ,
• Leandro Sabei 5 ,
• Michelle S. Araujo 5 ,
• Tauana Maria de Paula 5 ,
• Adroaldo J. Zanella 5 ,
• Mark A. Holmes   ORCID: orcid.org/0000-0002-5454-1625 2 ,
• James L. N. Wood   ORCID: orcid.org/0000-0002-0258-3188 2 &
• Andrew Balmford   ORCID: orcid.org/0000-0002-0144-3589 1  

Nature Food ( 2024 ) Cite this article

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• Agroecology
• Agriculture
• Antibiotics
• Climate-change mitigation
• Environmental impact

Farming externalities are believed to co-vary negatively, yet trade-offs have rarely been quantified systematically. Here we present data from UK and Brazilian pig production systems representative of most commercial systems across the world ranging from ‘intensive’ indoor systems through to extensive free range, Organic and woodland systems to explore co-variation among four major externality costs. We found that no specific farming type was consistently associated with good performance across all domains. Generally, systems with low land use have low greenhouse gas emissions but high antimicrobial use and poor animal welfare, and vice versa. Some individual systems performed well in all domains but were not exclusive to any particular type of farming system. Our findings suggest that trade-offs may be avoidable if mitigation focuses on lowering impacts within system types rather than simply changing types of farming.

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A 12% switch from monogastric to ruminant livestock production can reduce emissions and boost crop production for 525 million people

Luxi Cheng, Xiuming Zhang, … Baojing Gu

Livestock farming generates major impacts. While it provides 30% of human dietary protein and 18% of calories 1 , it occupies 75% of agricultural land 2 , emits 14–17% of anthropogenic greenhouse gas (GHG) emissions 3 , 4 and uses more antimicrobials than human medicine 5 . Livestock production is rapidly growing 6 , especially pig production, which has quadrupled in the past 50 years ( www.fao.org/faostat ). Externalities of farming—consequences that affect external parties—are thought to trade off with one another, with types of farm that perform well in one domain performing consistently poorly in others 7 , 8 , 9 , 10 , 11 , 12 , 13 . However, different externalities are typically examined in isolation, and associations among them have only been empirically and systematically quantified for narrow sets of systems and externalities 9 , 14 , 15 . The few studies that consider how externalities co-vary across contrasting production systems find trade-offs are less common than typically perceived. For example, a negative association was found between freshwater use and GHGs and between freshwater use and eutrophication in tomato systems, although GHGs and eutrophication had a positive association 16 . Positive associations have been shown between GHG and soil organic carbon costs in Chinese grain 17 and among GHGs, acidification and eutrophication in Iranian wheat and barley 18 . However, only a small subset have been carried out in livestock systems. These have reported positive associations among land-use, GHG, nitrogen, phosphorus and soil costs for European dairy; between land-use and GHG costs for Brazilian beef 9 ; and between GHG, acidification, eutrophication, non-renewable energy and land-use costs for European beef 19 . Reference 20 found positive associations between GHG, land use, water use and fossil fuel costs in Brazilian beef but trade-offs among these costs with metal depletion and acidification costs. One other study 21 examined animal welfare and antimicrobial use (AMU) costs and found no association in Italian dairy farms. Importantly, none has included both environmental costs and animal welfare 9 , 22 , 23 , 24 . Without understanding the associations among externalities, it is impossible to provide informed guidance about reducing the negative effects of farming 8 . Currently, for some externalities the impacts of different practices are frequently assumed, and in some cases effectively ignored, with potentially counterproductive consequences.

In this Article, we examine four critically important externalities that have never been systematically measured together across any farming system. Our analysis provides empirical data allowing the systematic comparison of many alternative farming systems and their consequences for land use (as an externality in its own right and as a proxy for biodiversity loss 25 ), GHG emissions, AMU (as a proxy for antimicrobial resistance) and animal welfare. We focus on pig production because pork is already the second most commonly eaten meat worldwide by mass, global pork production has grown at 1.2% yr −1 since 2005 ( www.fao.org/faostat ), and this growth is predicted to continue through to 2050 (ref. 26 ). We focus on four externality costs where urgent mitigation is needed and where pig production imposes substantial burdens: land use, because pig production already uses 8.5% of arable land 27 ; GHG emissions, because pig production accounts for 9% of GHGs from livestock 4 ; AMU as pig production uses more antimicrobials than any other livestock sector 28 and the livestock sector uses twice as much as human medicine 29 ; and welfare because pigs are sentient and intelligent 30 . Our large primary dataset has allowed us to examine all four of our focal externalities. Our study farms spanned most of the world’s commercial pig farm types, and our data represent the annual production of over 1.2 million pigs. Our objectives were to quantify four externalities of central importance to society (land use 25 , GHG emissions 31 , AMU 32 and animal welfare 33 ) across a broad range of contrasting farms, to test whether these externalities trade off against each other and to identify those best- and worst-performing systems and types of system.

Externality cost estimates

Each of our 74 UK and 17 Brazilian data points corresponded to a real-world, commercial breed-to-finish system made up of one to three farms. Each externality was aggregated across the whole lifecycle of production (see Methods for further details) expressed per functional unit (per kilogram deadweight (DW)) and, for clarity, is referred to as a cost where a high value indicates a more harmful outcome.

Land-use costs across both UK and Brazilian systems varied by a factor of 12 from 3.0 to 35.8 m 2  yr kgDW −1 , and GHG costs varied by a factor of 9 from 6.2 to 55.9 kgCO 2 e kgDW −1 (1.3 to 12.2 kgCO 2 e kgDW −1 if excluding forgone sequestration; Fig. 1 ). We used two AMU metrics which are both reported in mg kgDW −1 : (1) total use, which ranged from 0 to 606 mg kgDW −1 , and (2) use of those critically important to human health 24 ( Methods ) which ranged from 0 to 65.7 mg kgDW −1 (Fig. 1 ). Animal-welfare costs ranged widely, with some systems assessed as harmful to welfare and others where the quality of life was deemed sufficiently high that they were assessed as being beneficial (Fig. 1 ).

We did not find significant differences in land-use and GHG costs between UK and Brazilian systems (Wilcoxon rank-sum test, P  > 0.2), although this may be due to the small sample of Brazilian farms. Brazilian systems had significantly higher total and critically important AMU costs and animal-welfare costs than UK systems (Wilcoxon rank-sum test, all P  < 0.01). In UK systems there were significant differences among label types in land-use costs (Kruskal–Wallis χ 2  = 23.3, d.f. = 5, P  < 0.01) with Organic costs higher than the Royal Society for the Prevention of Cruelty to Animals (RSPCA) assured, Red tractor and ‘none’ (post hoc Dunn’s analyses all P  < 0.01). There were also significant differences in GHG costs ( χ 2  = 21.9, d.f. = 5, P  < 0.01) with Organic costs higher than RSPCA assured, Red tractor and ‘none’ (post hoc Dunn’s analyses P  < 0.01, P  > 0.01 and P  = 0.02, respectively; see Extended Data Fig. 1 for results excluding forgone sequestration), and total AMU costs (Kruskal–Wallis χ 2  = 11.7, d.f. = 5, P  = 0.04; note that no pairwise comparisons were significant—all P  > 0.2—and that critically important AMU costs did not differ among label types; Extended Data Fig. 2 ). There were significant differences among label types for animal-welfare costs (Kruskal–Wallis χ 2  = 34.5, d.f. = 5, P  < 0.01) with greater costs in ‘none’ and Red tractor systems than in woodland and Organic (post hoc Dunn’s P  = 0.01, P  < 0.01 and both P  < 0.01, respectively), and in Red tractor than free range ( P  = 0.01). There were also significant differences in some costs among husbandry types (Extended Data Figs. 3 – 7 ).

UK externality costs with positive associations

Land-use and GHG costs of contrasting UK pig production systems were strongly positively associated with one another (Fig. 2a ). This association remained when GHG costs excluded forgone sequestration (Spearman rank r s  = 0.84, P  < 0.01; Supplementary Fig. 1 ). Animal-welfare costs were moderately positively associated with AMU costs (Fig. 2b ), and we found no significant association between animal-welfare costs and critically important AMU costs (Extended Data Fig. 8 ).

Three assurance schemes (Organic, the welfare-focused RSPCA assured and traceability-focused Red tractor) as well as two other non-assurance product labels, free range and woodland. a , Land-use costs, including land to rear pigs and to grow their feed. b , GHG costs, including emissions associated with feed production, enteric fermentation, manure management, energy, fuel, transport, slaughter, processing and following recent assessments 9 , 52 , 53 , 63 , 70 , 71 also included forgone sequestration—the carbon opportunity cost which accounts for the difference in carbon stored in farmed versus non-farmed land. c , Total AMU costs (for data on AMU of greatest importance to human health 24 , see Extended Data Fig. 2 ). d , Animal-welfare costs using methods set out in ref. 64 , which weighted the quantity of life-years required to produce 1 kg DW by quality-of-life scores based on detailed animal-based welfare assessments we carried out for each system ( Methods ). The shapes and colours of scattered points show the husbandry type of breeding and finishing subsystems, respectively. Letters above boxplots show Dunn’s post hoc results, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences ( P  < 0.05). Analyses were carried out on a subset of our data ( n  = 43) with one datapoint selected randomly from those that shared breeding and/or rearing herds ( Methods ). Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges, respectively. The middle horizontal bar is the median.

a , b , Land-use and GHG costs ( a ) and AMU and animal welfare costs 64 ( b ), with higher externality costs representing increasingly negative outcomes. Note the break in the animal-welfare cost axis due to the very low cost of one system. r s and P values were from two-sided Spearman rank correlations on a subset of our data ( n  = 43) with one datapoint selected randomly from those that shared breeding and/or rearing herds ( Methods ).

UK externality costs with negative associations

In contrast to these positive associations, AMU and animal welfare both had moderate negative associations with both GHG and land-use costs (Fig. 3 )—systems that performed well according to one pair of externalities and typically performed poorly in terms of the other pair of measures of impact. Although some associations are not simple negative linear trends (Fig. 3a,c ), these patterns suggest that in broad terms there were trade-offs. Pig systems either performed well in land use and GHGs and poorly for animal welfare and AMU, or vice versa. However, it is important to examine not just overall relationships but to look at individual systems as well.

a – d , Associations between land-use cost and total AMU cost ( a ), between land-use cost and animal-welfare cost ( b ) between GHG cost and total AMU cost ( c ) and between GHG cost and animal-welfare cost ( d ) across 74 UK pig systems. r s and P values were from two-sided Spearman rank correlations on a subset of our data ( n  = 43) with one datapoint selected randomly from those that shared breeding and/or rearing herds. Note that there are breaks in the animal-welfare cost axes due to the very low costs of one system.

UK systems with co-benefits

Focusing on those systems ranked in the best-performing 50% of our sample for each cost, we can see that these broad trade-offs are not inevitable. For each pair of negatively associated externalities, several systems did not fit the overall negative association and instead combined low costs in both externalities (Fig. 4 ). Importantly, the best-performing systems overall were spread across different types of pig farming, but no type performed consistently well. Of the five systems in the best-performing 50% for all four externalities (shown as triangles in Fig. 4 ), three were RSPCA assured systems (of 27 in this study) that were outdoor bred and straw-yard finished, one was a fully outdoor woodland system (of three assessed here) and one was a Red tractor system (of 61) with hybrid indoor–outdoor breeding and slatted finishing. This list of consistently best-performing systems did not change if forgone sequestration was not included in our estimate of GHG costs. A further 10 systems were in the best-performing 50% by three externality costs: these ranged from indoor-bred, slatted-finishing systems with no labelling through to outdoor-bred, straw-yard finished RSPCA assured systems; in this case excluding forgone sequestration from GHG costs did slightly alter which systems met this threshold (Supplementary Table 1 ). Of these, six did not meet the benchmark of being in the best 50% for welfare cost, three did not meet the AMU cost benchmark and one system did not meet the GHG cost benchmark. It is noteworthy that no Organic nor free-range systems appeared in the best-performing 50% in three or more domains as none was in the best 50% for either land-use nor GHG cost. However, 100% of the six Organic systems and 61% of 18 free-range systems were in the best-performing 50% for both welfare and AMU, as were all three woodland systems. One system was in the best-performing 25% for all externality costs: an RSPCA assured system with outdoor breeding and straw-yard finishing.

a – f , Each plot shows UK breed-to-finish pig systems in the top 50% for both respective externality costs: land use and GHG ( a ), AMU and animal welfare ( b ), land use and AMU ( c ), land use and animal welfare ( d ), GHG and AMU ( e ), and GHG and animal welfare ( f ). The five systems shown as triangles are those that performed in the best 50% for all four externality costs.

All label and husbandry types that had systems in the best-performing 50% also had systems that were in the worst-performing half in at least one domain (Extended Data Fig. 9 ). Four systems were in the bottom 50% for all four externalities: three Red tractor systems and one RSPCA assured system; one Red tractor system was in the bottom performing 25% for all externalities. No type was consistently associated with low costs across our four domains, which has important implications for consumers as labelling does not allow them to make informed decisions about these externalities.

Examination of Brazilian pig production

These broad insights from UK systems were generally echoed when comparing 17 Brazilian pig systems. We found a significant positive association between land-use cost and GHG cost (which again remained when forgone sequestration was excluded; Supplementary Fig. 2 ) and some evidence of a positive association between AMU and animal-welfare cost (Fig. 5 ; note that colours refer to husbandry types as there are no established labels in Brazil). We found less clear associations than in the UK between other pairs of externalities, which may be due to our smaller sample size. However, once again we found some systems—in this case three—that were in the top 50% for all four externality costs and a further five in the top 50% for three costs. These results suggest that, as in the UK, trade-offs among contrasting externality costs are not consistent in Brazilian pig production and that there are systems that perform above average across all four of the costs we measured.

a – f , The associations among land-use and GHG costs ( a ), AMU and animal-welfare costs ( b ), land-use and AMU costs ( c ), land-use and animal-welfare costs ( d ), GHG and AMU costs ( e ) and GHG and animal-welfare costs ( f ). r s and P values are from two-sided Spearman rank correlations on a subset of our data ( n  = 8) with one data point selected randomly from those that shared breeding and/or rearing herds. Our sample was too small to identify significant differences among husbandry types.

The costs of switching pig systems at scale

The consequences of changes in farm types can be illustrated by considering the hypothetical externality costs of meeting all of current UK pig production through a single system. Meeting 2021 UK pig production (based on Agriculture and Horticulture Development Board figures; https://ahdb.org.uk/uk-pig-facts-and-figures ) entirely via Organic production, based on its median externality costs from this study, would result in considerably less AMU (88–98% less, an overall reduction of 8–56 tonnes of active ingredient per year) and substantial welfare improvements, compared with production from entirely Red tractor, RSPCA assured systems or systems with no label type. However, Organic systems would have three times as much impact on the climate as Red tractor, RSPCA assured systems or systems with no label type (an additional 25 million tonnes of CO 2 equivalents per year; 2.6 million tonnes excluding forgone sequestration) and use four times as much land (an additional 1.7 million hectares per year; ~10% of UK agricultural land).

The exceptional scale of impacts of global agriculture, and particularly livestock production, makes it imperative to search for farming systems that impose low externality costs. We tested the associations, across different methods of livestock production, of key externalities which are commonly perceived to trade off with one another. We found evidence that while trade-offs do indeed exist, they were not inevitable. Comparing systems that span most commercial pig production found across the world, we find that systems with low land use typically have low GHG costs but high AMU costs and poor welfare. This finding aligns with common perceptions 34 , 35 , 36 , 37 , which were until now based on assumption. However, this overall view masks the equally important finding that some systems achieve low externality costs across all domains. Notably, we find no unique characteristics among the best- or worst-performing systems and that no label or husbandry type is a reliable indicator of neither these best- nor worst-performing systems.

Previous studies focus on externalities in isolation and have small sample sizes, but when adjusted for functional units and system boundaries, our results for individual externalities align well for land-use 38 , GHG 23 , 39 , AMU 28 , 40 and animal-welfare costs 41 .

How different groups of stakeholders prioritize, value and balance different externalities would determine which farm and label types would result in the best outcomes. Our findings suggest that mitigation should not simply focus on changing broad types of farming but must also include lowering impacts within system types. Within each farming type, we find there is substantial scope for improvement—for example, land-use costs range by more than a factor of 5 within woodland systems. Disappointingly, we find that our current ways of classifying UK pig systems (and hence marketing pork) are not identifying and promoting the best-performing systems for land use, the climate, AMU and animal welfare. This means our current labelling systems are not helping consumers or indeed regulators to identify systems that perform well across multiple areas of societal concern. We suggest that interventions may be more effective if they focus on measurable improvements in outcomes rather than changes in farm types.

Our analyses may be limited by the size of our sample of farms, their geographic range and their spread across farm types. Despite the large and diverse sample of pig production systems covered by our dataset, we were still only able to recruit, for example, three commercial woodland farms. Nevertheless, our sample was sufficient to identify promising systems that perform better than average in terms of each of land-use, GHG, AMU and animal welfare costs. We can also be confident in our identification that free range and Organic pig production rarely if ever perform well across all four domains, as our study had very high coverage of these sectors (60% and 47% of UK production, respectively; Methods ) and no farm we examined from either group was in the top 50% for land-use or GHG costs. This finding is in contrast to common perceptions of these types of system 42 .

Our findings are also inevitably limited by data quality. Due to strict regulations in the UK on record keeping (of AMU, livestock transport, feed use and so on), we are confident that our UK AMU data are reasonably accurate. In Brazil, the quality of antimicrobial record keeping was more variable. Fortunately, excluding those systems with poorer-quality records did not alter our conclusions (Supplementary Fig. 3 ). Other sources of uncertainty are likely in both countries—from imputing missing data ( Methods ), from our choice of crop land and GHG footprint data, from our choice of emissions factors, because of non-reporting of unfinished antimicrobials (which probably led to slight over-estimation of AMU costs) and because of the snapshot approach of our welfare assessments. In addition, our land-use cost estimates did not account for differences in the impacts of land use on biodiversity (except that under tree cover; see Methods for explanation) as it was only possible to obtain sourcing information at the level of country of origin for many feed crops, and methods for comparing biodiversity impacts of agricultural products are rudimentary and limited in their regional specificity 43 . It will be important to account for these differences in future analyses when robust methods for equating the biodiversity opportunity costs of land use are available.

We are limited by the number of externalities we could practically examine. Future work should consider and incorporate additional environmental externalities important in pig production such as eutrophication and acidification 44 . It also would be valuable to expand the scope of analyses such as this to consider broader social outcomes, such as the effects of pork consumption on public health, the financial viability and scalability of contrasting production systems, and their implications for the well-being of farmers.

In summary, we suggest that our results confirm that in seeking to increase the sustainability of agriculture, it is not enough to assume relationships between externalities or even simply to look at general trends based on high-quality data. We need instead to consider individual farms, identify those that appear best at limiting externality costs across a broad range of outcomes of societal concern, and understand, promote and incentivize their practices. We hope that the present work encourages others to undertake similar but complementary studies, covering more externalities and, critically, other important but poorly documented agricultural sectors. We believe such analyses are essential for enabling the identification and promotion of the most promising options for mitigating the impacts of food production systems.

Sampling and classification of farms

We contacted 150 UK and 30 Brazilian pig producers, of which 44 and 20 producers participated in this study, respectively. We contacted farmers with the help of collaborating industry professionals, internet searches and social media. Sample bias was minimized by actively recruiting farm types that might otherwise be underrepresented with the help of experts. Our dataset consisted of breed-to-finish systems, which each involved one to three farms (see Extended Data Fig. 10 for a visualization of this), and some producers had more than one farm. Our final dataset consisted of 74 UK and 17 Brazilian data points—each a breed-to-finish system, with a unique finishing or fattening farm, but some shared breeding and/or rearing farms, so our data points are not all independent. We accounted for this in our analysis (‘Statistical analyses’).

We classified UK systems by labelling type (taken here as membership of the quality assurance schemes Red tractor, RSPCA assured and Organic, and two other non-assurance labels—free range and woodland; and by husbandry type, how the pigs are housed by breeding and finishing system; Supplementary Table 2 ). The Brazilian pig sector does not have such well-established assurance schemes but has globally important husbandry types not present in the UK such as systems with gestation crates 45 and that use growth promoters (for example, ractopamine). We therefore classified Brazilian systems only by husbandry type (Supplementary Table 3 ).

Data collection and allocation

Each farm was visited between September 2017 and December 2020. We conducted welfare assessments and questionnaire-based interviews with farm managers to collect data for simultaneously estimating land-use, GHG, AMU and animal-welfare costs for the most recent year of available data. Where upstream farms did not exclusively supply one finishing or fattening farm, externality costs were allocated proportionately by the number of pigs entering and leaving each farm. For example, if a breeding farm produced 1,000 weaned piglets and 500 of these moved to the finishing or fattening farm in question, 50% of the costs of the breeding farm were allocated to the finishing or fattening farm.

Externality cost quantification

The system boundaries ( Supplementary Methods ) included feed production, pig production from breeding to finishing, slaughter and processing. All externalities were measured per functional unit (per kilogram DW) which is key to comparing the costs of producing a given amount of food in different ways 9 . The kg DW was calculated using data collected via the interview on productivity, animal numbers and either DW output or liveweight and dressing percentages. DW included meat from finishing pigs and sows sent to slaughter, which were equated with economic allocation using mean prices between September 2018 and December 2020 from the UK Agriculture and Horticulture Development Board ( https://ahdb.org.uk/pork/gb-deadweight-pig-prices-uk-spec ) and a large British pork processor (anonymous, personal communication) for UK systems. Similar data were not available for Brazilian pig production, so this same allocation was used. See Supplementary Methods for equations used to calculate externality costs.

Land-use cost

Land-use cost was the total area of land required over 1 year to produce 1 kg DW of pork in m 2  yr kgDW −1 and includes land to rear pigs and grow their feed. Field studies examining the density of >2,000 wild species of vertebrates, flowering plants and insects on five continents have found that farming would have least impact on biodiversity if high-yield production was coupled with sparing remaining land for nature 9 . We, therefore, considered land use not just as a direct environmental impact in itself but also as a proxy for impact on biodiversity, with low land-use costs (high yields) representing lower impacts on biodiversity.

Land-use cost to rear pigs

The area of land used to rear pigs was obtained via the questionnaire or calculated from a map. Pigs kept in woodland are argued to have a positive impact on biodiversity 46 , 47 , although there is limited empirical evidence to support this. Therefore, we cautiously assumed this land to have equal biodiversity value to natural habitat without pigs, and so excluded land used to rear pigs under tree cover from our land-use costs. The observation that one of our woodland systems was in the best 50% for all externalities was dependent on this assumption—when land under woodland was not excluded, it was no longer in the top 50% for land-use cost.

Land-use cost to grow feed

The area of land used to grow feed was calculated using farm- and production-stage specific feed formulations (which cannot be shared due to Intellectual Property constraints) and the total amount of each feed used in the most recent year. For the small portion of feeds for which we could not obtain precise formulations (1.5% by mass for UK systems and 1.1% for Brazilian systems), the most similar formulation was used instead. Feed ingredient yields were obtained from FeedPrint 48 , using country-specific data where possible. Where the country of origin was unknown or unavailable, the most similar feed or country with available data was used. Some feed formulations reported aggregated rather than individual ingredients. These were again gap-filled using information from the most similar feeds. For example, some feeds had added synthetic amino acids, but the formulation was not broken down into specific amino acids. In this case, feed amino acid contents were matched to the total amino acid levels of a similar feed using data from feedtables.com. For organic feed ingredient yields, we applied percentage differences in conventional versus organic yields for wheat, barley, oats and beans from ref. 49 , maize from ref. 50 and soya and peas from ref. 51 . Where feeds were co-products, economic allocation was used to assign costs to the focal feed using data from FeedPrint.

GHG cost was the mass of CO 2 e (in kilograms) emitted in the production of 1 kg DW. This included emissions from feed production (including fertilizer manufacture), the pigs, fuel, energy, transport, slaughter and meat processing and also forgone sequestration (the carbon opportunity cost of land use 9 , 52 , 53 ). Methane and nitrous oxide were converted into CO 2 e using GWP100.

GHG cost from feed

Feed emissions included those associated with feed ingredient production, milling and processing, and were obtained via Feedprint, using country-specific values where possible. Where there were gaps in our data, they were filled as with land-use costs. GHG emissions associated with producing organic feeds were calculated by applying percentage differences in GHG footprints of conventional versus organic GHGs using data from Williams et al. 54 . No Brazilian farms used organic feeds.

GHG cost from pigs

Emissions from pigs themselves included enteric methane, and nitrous oxide and methane from manure management. Enteric manure methane was calculated using emissions factors from https://naei.beis.gov.uk/data/ef-all for the UK and Intergovernmental Panel on Climate Change tier 1 emissions factors for Brazil 55 . Manure methane was calculated using the gross energy intake we calculated using farm and production-stage specific feeds (using data on gross energy of feed ingredients from feedtables.org) and activity data and emissions factors from ref. 55 . Direct and indirect nitrous oxide emissions were calculated from nitrogen input (which we calculated using farm and production stage-specific feeds) using nitrogen content of feed from feedtables.org and nitrogen utilization rates from ref. 56 . Residual manure nitrogen was assumed to displace fertilizer use in feed production as in Weidema et al. 57 (Ecoinvent v3. vol. 3; http://www.ecoinvent.org/ , 2014). GHG costs were insensitive to contrasting approaches to accounting for displaced fertilizer (see Supplementary Fig. 4 for a sensitivity analysis).

GHG cost from fuel, electricity and transport

Annual use of fuel and electricity were converted into kg CO 2 using emissions factors from ref. 58 and www.carbonfootprint.com . Emissions from travel were calculated for a return journey per tonne-km transported using emissions factors from ref. 59 for Brazil and ref. 60 for the UK. Where farm-specific data were available (that is, producers reported the location of the feed mill/crops) these were used. If only the region (municipality, state) was known, the centroid of this was taken. Where regions were unknown, we used the country- and crop-specific mean distance travelled for other farms in this study that did not grow their own feed. It was not possible to gain information for transport upstream of feed farms—that is, from crop farms to feed mills—so FeedPrint values were used. This includes transport emissions associated with manure spreading on feed crops.

GHG cost from slaughter and processing

Emissions from slaughter and meat processing were assumed to be the same as in ref. 61 .

GHG cost from forgone sequestration

As has been explored in several recent papers, farming carries an additional GHG cost—that of forgone sequestration, because the carbon stored in farmed land is less than that of native habitat that could in principle occupy that land in the absence of farming 9 , 53 . Forgone sequestration was calculated using values for annual aboveground biomass accrual (<20 years) taken from ref. 55 , assuming the relevant native habitat in the area used for production. In the UK this was ‘Temperate Oceanic Forest—Europe’, except for sorghum and soybean products which were assumed to be ‘Tropical Moist Deciduous Forest—North and South American’. For Brazilian systems, we used accrual rates for ‘Tropical Moist Deciduous Forest—North and South America’. Changes in soil carbon were taken from the mean percentage change in soil organic carbon from crop to secondary forest from ref. 62 . Initial carbon values were taken from ref. 55 , and changes were assumed to take 20 years, following Intergovernmental Panel on Climate Change guidelines. We assumed soils in the UK were ‘cold temperate, moist, high activity soils’ except for sorghum and soybean products which were assumed to be ‘subtropical, humid, low activity soils’, and in Brazil were ‘subtropical, humid, low activity soils’. See Extended Data Fig. 1 and Supplementary Figs. 1 and 2 for GHG costs excluding forgone sequestration. Our results for GHG costs were broadly insensitive to contrasting approaches to accounting for forgone sequestration (see Supplementary Fig. 5 for a sensitivity analysis testing the effect of halving sequestration rates) 63 .

AMU cost was the milligrams of antimicrobial used to produce 1 kg DW. AMU cost was calculated from medicines records for the most recent year of available data obtained via the questionnaire. Reference 24 found it was important to report both the total use and use of critically important antimicrobials for human health because the latter tend to be lightweight and therefore easily obscured by total use metrics. We focus on total use metrics to enable comparison with other studies as these are the most commonly used metrics. However, we also report critically important use as use of category B antimicrobials according to the European Medicines Agency (Extended Data Figs. 2 , 6 and 8 ).

Animal-welfare cost

We estimated the animal-welfare cost of each pig production system as the number of life years required to produce 1 kg DW weighted by quality-of-life scores—calculated in the same way as ref. 64 , a study we conducted to identify LCA-compatible animal welfare metrics which built upon WQ (Welfare Quality) assessments. WQ assessments are methods for quantifying quality-of-life based on extensive, largely animal-based, observations. H.B. (who is WQ certified) conducted assessments for breeding pigs and pre-weaning piglets and for fattening pigs for each of our data points. WQ assessments involve the systematic measurement of more than 30 different welfare indicators for each system, including a range of indicators of health and behaviour. Protocols were carried out according to guidelines except where sows were kept in crates for part or all of gestation. During the time sows were kept in crates, as there are no opportunities for social interactions, the worst possible score for social behaviour was given. WQ assessments result in four principle scores out of 100, for good health, good feeding, good housing and appropriate behaviour. There is no consensus on how these principles should be weighted when combined into an overall score. Reference 64 found system rankings were very little affected by the weighting approach, so we used a simple weighting of 0.35, 0.25, 0.15 and 0.25, respectively. The assessments for sows and piglets and for fattening pigs were combined by multiplying the quantities of life years required by each to produce 1 kg DW by their respective WQ scores and then summing them. There are a broad range of attitudes on what criteria determine good welfare 65 , 66 , 67 , 68 —which could mean welfare is viewed in some cases as an externality benefit rather than cost. Reference 64 found the relative performance of label types and husbandry types was very largely insensitive to the choice of transition from costly to beneficial welfare, so here we chose a metric with an intermediate approach where WQ principle scores of ≥80 are deemed to be beneficial to welfare, and so time experiencing them was treated as a benefit (negative cost) and lower WQ principle scores as costly.

Statistical analyses

Some of our data points were not independent of one another as they shared breeding or rearing farms. There were insufficient data to remove the effects of this statistically, so where statistics are reported, this is for a subset of our data (UK, n  = 43; Brazil, n  = 8) with one datapoint randomly selected from those that share breeding and/or rearing farms. We used Spearman rank correlations to characterize the associations between externality costs, and to compare farm types we used Wilcoxon rank-sum tests and Kruskal–Wallis tests with post hoc Dunn’s analysis using the Holm method to control for multiple comparisons. Analyses were carried out in RStudio4.1.1 using the packages ‘stats’, ‘FSA’, ‘g’gpubr’, ‘rcompanion’, ‘ggthemes’, ‘patchwork’ and ‘ggplot2’.

Ethical approval was given by the Human Biology Research Ethics Committee (application number 2018.22) at the University of Cambridge and by Plataforma Brasil before commencement. Before participating in the study, all farmers gave informed consent.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data can be found in Figshare repository at https://doi.org/10.6084/m9.figshare.22760723 (ref. 69 ).

Code availability

Code is available upon request.

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Acknowledgements

H.B. was supported by the Biotechnology and Biological Sciences Research Council Doctoral Training Partnership at the University of Cambridge (BB/M011194/1) and is a part of the Future of Food program at the Oxford Martin School; A.B. was supported by a Royal Society Wolfson Research Merit award (WM160065); M.A.H. was supported by the Medical Research Council (MR/N002660/1); and J.L.N.W. was supported by The Alborada Trust. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to all the farmers who participated in this study and everyone who helped us in recruiting them. We cannot state your names to retain anonymity, but this study would not have been possible without you.

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Smith School of Enterprise and Environment, University of Oxford, Oxford, UK

Harriet Bartlett

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Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, University of São Paulo, São Paulo, Brazil

Márcia Zanella, Beatriz Kaori, Leandro Sabei, Michelle S. Araujo, Tauana Maria de Paula & Adroaldo J. Zanella

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All authors designed the project. H.B. collected data from UK farms; H.B., M.Z., B.K., L.S., M.S.A. and A.J.Z. collected the data from Brazilian farms. H.B. conducted analyses and drafted the manuscript. All authors contributed to the revision of the manuscript.

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Extended data

Extended data fig. 1 ghg costs of 74 uk breed-to-finish pig systems by label type excluding foregone sequestration..

GHG costs varied significantly among label types (Kruskal-Wallis: χ 2  = 16.0, df=5, p < 0.01, n = 43), with Organic costs being significantly higher than RSPCA assured and Red tractor (post-hoc Dunn’s analysis p = 0.02 and p = 0.01 respectively). Letters above boxplots show the results from Dunn’s post-hoc tests, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences between median values. Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 2 Critically important AMU costs of 74 UK breed-to-finish pig systems by label type.

Critically important AMU costs did not vary significantly by label type (Kruskal-Wallis p = 0.1; n = 43). Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 3 Land-use costs of 74 UK breed-to-finish pig systems by a) breeding and b) finishing husbandry type.

This included land to rear animals and produce feed. The shapes and colours of scattered points show the husbandry type of breeding and finishing subsystems respectively. There were significant differences in land-use costs among husbandry types (Kruskal-Wallis: χ 2  = 11.1, df=2 and χ 2  = 23.3, df=4 for breeding and finishing system respectively, both p < 0.01, n = 43). Letters above boxplots show the results from Dunn’s post-hoc tests, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences between median values. Indoor-bred systems had lower land-use costs than outdoor bred (p < 0.01), and outdoor-finished than straw yard (p = 0.01) and slatted (p < 0.01). Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 4 GHG costs of 74 UK breed-to-finish pig systems by a) breeding and b) finishing husbandry type.

GHG cost included animal source emissions, those associated with feed production, transport, energy use, slaughter, processing and forgone sequestration. The shapes and colours of scattered points show the husbandry type of breeding and finishing subsystems respectively. There were significant differences in GHG costs among husbandry types (Kruskal-Wallis: χ 2   =  9.2, df=2, p  =  0.01 and χ 2   =  18.4, df=4, p  >  0.01 for breeding and finishing system respectively, both n  =  43). Letters above boxplots show the results from Dunn’s post-hoc tests, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences between median values. Indoor-bred systems had lower GHG costs than outdoor bred (p  <  0.01), and outdoor-finished than straw yard and slatted (both p  <  0.01). Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 5 AMU costs of 74 UK breed-to-finish pig systems by a) breeding and b) finishing husbandry type.

There were significant differences between breeding husbandry types (χ 2   =  7.31, df=2, p  =  0.03, n  =  43) but not finishing husbandry types (Kruskal-Wallis p  =  0.1, n  =  43). Post-hoc Dunn’s analyses did not find any pairs with significant differences. Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 6 Critically important AMU costs of 74 UK breed-to-finish pig systems by a) breeding and b) finishing husbandry type.

There were significant differences between breeding husbandry types (χ 2  = 10.9, df=2, p < 0.01, n = 43) but not finishing husbandry types (Kruskal-Wallis p = 0.1, n = 43). Post-hoc Dunn’s analyses found that critically important AMU cost was higher in indoor-bred systems compared with outdoor-bred systems. Letters above boxplots show the results from Dunn’s post-hoc tests, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences between median values (p > 0.05). Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 7 Animal welfare costs of 74 UK breed-to-finish pig systems by a) breeding and b) finishing husbandry type.

There were significant differences among costs by husbandry type (Kruskal-Wallis by breeding type χ 2  = 32.1, df=2, p < 0.01 and husbandry type χ 2  = 31.4, df=4, p < 0.01, both n = 43). Post-hoc Dunn’s analyses found indoor-bred systems had higher costs than outdoor-bred systems (p < 0.01), and slatted-finished systems had higher costs than outdoor-finished (p > 0.01). Letters above boxplots show the results from Dunn’s post-hoc tests, controlled for multiple comparisons using the Holm method, with different letters indicating significant differences between median values. Upper and lower hinges correspond to the first and third quartiles. Upper and lower whiskers extend to 1.5 times the interquartile range from upper and lower hinges respectively. The middle horizontal bar is the median.

Extended Data Fig. 8 Critically important AMU and welfare costs of UK breed-to-finish pig systems.

Critically important AMU is just use of those antimicrobials most important to human health – Category B use according to the European Medicines Agency. r s and p values are from two-sided Spearman rank correlations on a subset of our data (n = 43) with one datapoint selected randomly from those that shared breeding and/or rearing herds (see Methods ).

Extended Data Fig. 9 The summed ranking of each of 74 UK breed-to-finish system by a) label type, and b) breeding system and (c) finishing system.

Each system was given an externality cost ranking from 1 for the best performing system to 74 for worst performing system, and these were summed across the four externalities. If a system was best in all four costs it would have a score of 4, and 296 if worst in all.

Extended Data Fig. 10 Diagram of which UK finishing and fattening farms shared breeding or rearing herds.

33 breed-to-finish systems did not share breeding/rearing herds with another – shown by single horizontal lines from breeding/rearing farm to finishing farms 1-33. 41 breed-to-finish systems did share breeding and/or rearing herds – shown by the multiple lines from breeding/rearing farm on the left leading to the multiple finishing farms 34-74 on the right. In our statistical analysis, one finishing farm was randomly selected from those that shared breeding and/or rearing farms – as illustrated by the different shades of blue: farms included in statistical analyses are shown in dark blue and those excluded in pale blue.

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Bartlett, H., Zanella, M., Kaori, B. et al. Trade-offs in the externalities of pig production are not inevitable. Nat Food (2024). https://doi.org/10.1038/s43016-024-00921-2

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