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Informal economy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Informal_economy

An informal economy (informal sector or grey economy) is the part of any economy that is neither taxed nor monitored by any form of government. Although the informal sector makes up a significant portion of the economies in developing countries, it is sometimes stigmatized as troublesome and unmanageable. However, the informal sector provides critical economic opportunities for the poor and has been expanding rapidly since the 1960s. Integrating the informal economy into the formal sector is an important policy challenge.

In many cases, unlike the formal economy, activities of the informal economy are not included in a country's gross national product (GNP) or gross domestic product (GDP). However, Italy has included estimates of informal activity in their GDP calculations since 1987, which swells their GDP by an estimated 18% and in 2014, a number of European countries formally changed their GDP calculations to include prostitution and narcotics sales in their official GDP statistics, in line with international accounting standards, prompting an increase between 3-7%. The informal sector can be described as a grey market in labour. Other concepts that can be characterized as informal sector can include the black market (shadow economy, underground economy), agorism, and System D. Associated idioms include "under the table", "off the books", and "working for cash".

Definition

The original use of the term 'informal sector' is attributed to the economic development model put forward in 1955 by W. Arthur Lewis, used to describe employment or livelihood generation primarily within the developing world. It was used to describe a type of employment that was viewed as falling outside of the modern industrial sector. An alternative definition from 2007 uses job security as the measure of formality, defining participants in the informal economy as those "who do not have employment security, work security and social security". While both of these definitions imply a lack of choice or agency in involvement with the informal economy, participation may also be driven by a wish to avoid regulation or taxation. This may manifest as unreported employment, hidden from the state for tax, social security or labour law purposes, but legal in all other aspects. In 2016 Edgar L. Feige proposed a taxonomy for describing unobserved economies including the informal economy as being characterized by some form of "non-compliant behavior with an institutional set of rules". He argues that circumvention of labor market regulations specifying minimum wages, working conditions, social security, unemployment and disability benefits gives rise to an informal economy, which deprives some workers of deserved benefits while conveying undeserved benefits to others.

The term is also useful in describing and accounting for forms of shelter or living arrangements that are similarly unlawful, unregulated, or not afforded protection of the state. 'Informal economy' is increasingly replacing 'informal sector' as the preferred descriptor for this activity.

Informality, both in housing and livelihood generation has historically been seen as a social ill, and described either in terms of what participant's lack, or wish to avoid. In 2009, the Dutch sociologist Saskia Sassen viewed the new 'informal' sector as the product and driver of advanced capitalism and the site of the most entrepreneurial aspects of the urban economy, led by creative professionals such as artists, architects, designers and software developers. While this manifestation of the informal sector remains largely a feature of developed countries, increasingly systems are emerging to facilitate similarly qualified people in developing countries to participate.

History

Governments have tried to regulate aspects of their economies for as long as surplus wealth has existed which is at least as early as Sumer. Yet no such regulation has ever been wholly enforceable.

Archaeological and anthropological evidence strongly suggests that people of all societies regularly adjust their activity within economic systems in attempt to evade regulations. Therefore, if informal economic activity is that which goes unregulated in an otherwise regulated system then informal economies are as old as their formal counterparts, if not older. The term itself, however, is much more recent.

The optimism of the modernization theory school of development had led people in the 1950s and 1960s to believe that traditional forms of work and production would disappear as a result of economic progress in developing countries. As this optimism proved to be unfounded, scholars turned to study more closely what was then called the traditional sector and found that the sector had not only persisted, but in fact expanded to encompass new developments. In accepting that these forms of productions were there to stay, scholars and some international organizations quickly took up the term informal sector (later known as the informal economy or just informality). The term Informal income opportunities is credited to the British anthropologist Keith Hart in a 1971 study on Ghana published in 1973, and was coined by the International Labour Organization in a widely read study on Kenya in 1972.

In his 1989 book The Underground Economies: Tax Evasion and Information Distortion, Edgar L. Feige examined the economic implications of a shift of economic activity from the observed to the non-observed sector of the economy. Such a shift not only reduces the government's ability to collect revenues, it can also bias the nation's information systems and therefore lead to misguided policy decisions. The book examines alternative means of estimating the size of various unobserved economies and examines their consequences in both socialist and market oriented economies. Feige goes on to develop a taxonomic framework that clarifies the distinctions between informal, illegal, unreported and unrecorded economies, and identifies their conceptual and empirical linkages and the alternative means of measuring their size and trends. Since then, the informal sector has become an increasingly popular subject of investigation in economics, sociology, anthropology and urban planning. With the turn towards so called post-fordist modes of production in the advanced developing countries, many workers were forced out of their formal sector work and into informal employment. In a 2005 collection of articles, The Informal Economy. Studies in Advanced and Less Developed Countries, the existence of an informal economy in all countries was demonstrated with case studies ranging from New York City and Madrid to Uruguay and Colombia.

An influential book on the informal economy is Hernando de Soto's El otro sendero (1986), which was published in English in 1989 as The Other Path with a preface by Peruvian writer Mario Vargas Llosa. De Soto and his team argued that excessive regulation in the Peruvian and other Latin American economies forced a large part of the economy into informality and thus prevented economic development. While accusing the ruling class of 20th century mercantilism, de Soto admired the entrepreneurial spirit of the informal economy. In a widely cited experiment, his team tried to legally register a small garment factory in Lima. This took more than 100 administrative steps and almost a year of full-time work. Feige's review of the Other Path places the work in the context of the informal economy literature. Whereas de Soto's work is popular with policymakers and champions of free market policies like The Economist, some scholars of the informal economy have criticized it both for methodological flaws and normative bias.

In the second half of the 1990s many scholars started to consciously use the term "informal economy" instead of "informal sector" to refer to a broader concept which includes enterprises as well as employment in developing, transition, and advanced industrialized economies.

Among the surveys about the size and development of the shadow economy (mostly expressed in percent of official GDP) are those by Feige (1989), and Schneider and Enste (2000) with an intensive discussion about the various estimation procedures of the size of the shadow economy as well as a critical evaluation of the size of the shadow economy and the consequences of the shadow economy on the official one. Feige´s most recent survey paper on the subject from 2016 reviewed the meaning and measurement of unobserved economies and is particularly critical of estimates of the size of the so-called shadow economy which employ Multiple Indicator multiple cause methods, which treat the shadow economy as a latent variable.

Characteristics

The informal sector is largely characterized by several qualities: skills gained outside of a formal education, easy entry (meaning anyone who wishes to join the sector can find some sort of work which will result in cash earnings), a lack of stable employer-employee relationships, and a small scale of operations. Workers who participate in the informal economy are typically classified as employed. The type of work that makes up the informal economy is diverse, particularly in terms of capital invested, technology used, and income generated.

The spectrum ranges from self-employment or unpaid family labor to street vendors, shoe shiners, and junk collectors. On the higher end of the spectrum are upper-tier informal activities such as small-scale service or manufacturing businesses, which have more limited entry. The upper-tier informal activities have higher set-up costs, which might include complicated licensing regulations, and irregular hours of operation. However, most workers in the informal sector, even those are self-employed or wage workers, do not have access to secure work, benefits, welfare protection, or representation. These features differ from businesses and employees in the formal sector which have regular hours of operation, a regular location and other structured benefits.

According to a 2018 study on informality in Brazil, there are three views to explain the causes of informality. The first view argues that the informal sector is a reservoir of potentially productive entrepreneurs who are kept out of formality by high regulatory costs, most notably entry regulation. The second sees informal forms as "parasitic forms" which are productive enough to survive in the formal sector but choose to remain informal to earn higher profits from the cost advantages of not complying with taxes and regulations. The third argues that informality is a survival strategy for low-skill individuals, who are too unproductive to ever become formal. According to the study the first view corresponds to 9.3 percent of all informal forms, while the second corresponds to 41.9 percent. The remaining forms correspond to low-skill entrepreneurs who are too unproductive to ever become formal. The author suggests that informal forms are to a large extent "parasitic" and therefore eradicating them (e.g., through tighter enforcement) could produce positive effects on the economy.

The most prevalent types of work in the informal economy are home-based workers and street vendors. Home-based workers are more numerous while street vendors are more visible. Combined, the two fields make up about 10–15% of the non-agricultural workforce in developing countries and over 5% of the workforce in developed countries.

While participation in the informal sector can be stigmatized, many workers engage in informal ventures by choice, for either economic or non-economic reasons. Economic motivations include the ability to evade taxes, the freedom to circumvent regulations and licensing requirements, and the capacity to maintain certain government benefits. A study of informal workers in Costa Rica illustrated other economic reasons for staying in the informal sector, as well as non-economic factors. First, they felt they would earn more money through their informal sector work than at a job in the formal economy. Second, even if workers made less money, working in the informal sector offered them more independence, the chance to select their own hours, the opportunity to work outside and near friends, etc. While jobs in the formal economy might bring more security and regularity, or even pay better, the combination of monetary and psychological rewards from working in the informal sector proves appealing for many workers.

The informal sector was historically recognized as an opposition to formal economy, meaning it included all income earning activities beyond legally regulated enterprises. However, this understanding is too inclusive and vague, and certain activities that could be included by that definition are not considered part of the informal economy. As the International Labour Organization defined the informal sector in 2002, the informal sector does not include the criminal economy. While production or employment arrangements in the informal economy may not be strictly legal, the sector produces and distributes legal goods and services. The criminal economy produces illegal goods and services. The informal economy also does not include the reproductive or care economy, which is made up of unpaid domestic work and care activities. The informal economy is part of the market economy, meaning it produces goods and services for sale and profit. Unpaid domestic work and care activities do not contribute to that, and as a result, are not a part of the informal economy.

Statistics

The informal economy under any governing system is diverse and includes small-scaled, occasional members (often street vendors and garbage recyclers) as well as larger, regular enterprises (including transit systems such as that of La Paz, Bolivia). Informal economies include garment workers working from their homes, as well as informally employed personnel of formal enterprises. Employees working in the informal sector can be classified as wage workers, non-wage workers, or a combination of both.

Statistics on the informal economy are unreliable by virtue of the subject, yet they can provide a tentative picture of its relevance. For example, informal employment makes up 58.7% of non-agricultural employment in Middle East – North Africa, 64.6% in Latin America, 79.4% in Asia, and 80.4% in sub-Saharan Africa. If agricultural employment is included, the percentages rise, in some countries like India and many sub-Saharan African countries beyond 90%. Estimates for developed countries are around 15%. In recent surveys, the informal economy in many regions has declined over the past 20 years to 2014. In Africa, the share of the informal economy has decreased to an estimate of around 40% of the economy.

In developing countries, the largest part of informal work, around 70%, is self-employed. Wage employment predominates. The majority of informal economy workers are women. Policies and developments affecting the informal economy have thus a distinctly gendered effect.

Estimated size of countries' informal economy

To estimate the size and development of any underground or shadow economy is quite a challenging task since participants in such economies attempt to hide their behaviors. One must also be very careful to distinguish whether one is attempting to measure the unreported economy, normally associated with tax evasion, or the unrecorded or non-observed economy, associated with the amount of income that is readily excluded from national income and produce accounts due to the difficulty of measurement. There are numerous estimates of tax noncompliance as measured by tax gaps produced by audit methods or by "top down" methods Friedrich Schneider and several co-authors claim to have estimated the size and trend of what they call the "shadow economy" worldwide by a currency demand /MIMIC model approach that treats the "shadow economy" as a latent variable. Trevor S. Breusch has critiqued the work and warned the profession that the literature applying this model to the underground economy abounds with alarming Procrustean tendencies. Various kinds of sliding and scaling of the results are carried out in the name of "benchmarking", although these operations are not always clearly documented. The data are typically transformed in ways that are not only undeclared but have the unfortunate effect of making the results of the study sensitive to the units in which the variables are measured.

The complexity of the estimation procedure, together with its deficient documentation, leave the reader unaware of how these results have been shorted to fit the bed of prior belief. There are many other results in circulation for various countries, for which the data cannot be identified and which are given no more documentation than "own calculations by the MIMIC method". Readers are advised to adjust their valuation of these estimates accordingly.

Edgar L. Feige finds that Schneider's shadow economy "estimates suffer from conceptual flaws, apparent manipulation of results and insufficient documentation for replication, questioning their place in the academic, policy and popular literature".

Comparison of shadow economies in EU countries

As of 2013, the total EU shadow economy had been growing to about 1.9 trillion € in preparation of the EURO driven by the motor of the European shadow economy, Germany, which had been generating approx. 350 bn € per year since the establishment of the Single Market in Maastricht 1993, (see diagram on the right). Hence, the EU financial economy had developed an efficient tax haven bank system to protect and manage its growing shadow economy. As per the Financial Secrecy Index (FSI 2013) Germany and some neighbouring countries, ranked among the world's top tax haven countries.

The diagram below shows that national informal economies per capita vary only moderately in most EU countries. This is because market sectors with a high proportion of informal economy (above 45%) like the construction sector or agriculture are rather homogeneously distributed across countries, whereas sectors with a low proportion of informal economy (below 30%) like the finance and business sector (e.g. in Switzerland, Luxembourg), the public service and personal Service Sector (as in Scandinavian countries) as well as the retail industry, wholesale and repair sector are dominant in countries with extremely high GDP per capita i.e. industrially highly developed countries. The diagram also shows that in absolute numbers the shadow economy per capita is related to the wealth of a society (GDP). Generally spoken, the higher the GDP the higher the shadow economy, albeit non-proportional.

There is a direct relationship between high self-employment of a country to its above average shadow economy. In highly industrialized countries where shadow economy (per capita) is high and the huge private sector is shared by an extremely small elite of entrepreneurs a considerable part of tax evasion is practised by a much smaller number of (elite) people. As an example German shadow economy in 2013 was 4.400 € per capita, which was the 9th highest place in EU, whereas according to OECD only 11.2% of employed people were self-employed (place 18). On the other hand, Greece's shadow economy was only 3.900 € p.c (place 13) but self-employment was 36.9% (place 1).

An extreme example of shadow economy camouflaged by the financial market is Luxembourg where the relative annual shadow economy is only 8% of the GDP which is the second lowest percentage (2013) of all EU countries whereas its absolute size (6.800 € per capita) is the highest.

Social and political implications and issues

According to development and transition theories, workers in the informal sector typically earn less income, have unstable income, and do not have access to basic protections and services. The informal economy is also much larger than most people realize, with women playing a huge role. The working poor, particularly women, are concentrated in the informal economy, and most low-income households rely on the sector to provide for them. However, informal businesses can also lack the potential for growth, trapping employees in menial jobs indefinitely. On the other hand, the informal sector can allow a large proportion of the population to escape extreme poverty and earn an income that is satisfactory for survival. Also, in developed countries, some people who are formally employed may choose to perform part of their work outside of the formal economy, exactly because it delivers them more advantages. This is called 'moonlighting'. They derive social protection, pension and child benefits and the like, from their formal employment, and at the same time have tax and other advantages from working on the side.

From the viewpoint of governments, the informal sector can create a vicious cycle. Being unable to collect taxes from the informal sector, the government may be hindered in financing public services, which in turn makes the sector more attractive. Conversely, some governments view informality as a benefit, enabling excess labor to be absorbed, and mitigating unemployment issues. Recognizing that the informal economy can produce significant goods and services, create necessary jobs, and contribute to imports and exports is critical for governments.

As the work in informal sector is not monitored or registered with the state, its workers are not entitled to social security, nor can they form trade unions. Informal economy workers are more likely to work long hours than workers in the formal economy who are protected by employment laws and regulations. A landmark study conducted by the World Health Organization and the International Labour Organization found that exposure to long working hours caused an estimated 745,000 fatalities from ischemic heart disease and stroke events in 2016. A systematic review and meta-analysis of health services use and health outcomes among informal economy workers, when compared with formal economy workers, found that these workers are less likely to use health services and more likely to have depression, highlighting their substantial health disadvantage.

Gender

In developing countries, most of the female non-agricultural labor force is in the informal sector. Female representation in the informal sector is attributed to a variety of factors. One such factor is that employment in the informal sector is the source of employment that is most readily available to women. A 2011 study of poverty in Bangladesh noted that cultural norms, religious seclusion, and illiteracy among women in many developing countries, along with a greater commitment to family responsibilities, prevent women from entering the formal sector.

Major occupations in the informal sector include home-based workers (such as dependent subcontract workers, independent own account producers, and unpaid workers in family businesses) and street vendors, which both are classified in the informal sector. Women tend to make up the greatest portion of the informal sector, often ending up in the most erratic and corrupt segments of the sector. In India, women working in the informal sector often work as ragpickers, domestic workers, coolies, vendors, beauticians, construction laborers, and garment workers.

According to a 2002 study commissioned by the ILO, the connection between employment in the informal economy and being poor is stronger for women than men. While men tend to be over-represented in the top segment of the informal sector, women overpopulate the bottom segment.Men are more likely to have larger-scale operations and deal in non-perishable items while few women are employers who hire others. Instead, women are more likely to be involved in smaller-scale operations and trade food items. Women are under-represented in higher-income employment positions in the informal economy and over-represented in lower-income statuses. As a result, the gender gap in terms of wage is higher in the informal sector than the formal sector. Labor markets, household decisions, and states all propagate this gender inequality.

Political power of agents

Workers in the informal economy lack a significant voice in government policy. Not only is the political power of informal workers limited, but the existence of the informal economy creates challenges for other politically influential actors. For example, the informal workforce is not a part of any trade union, nor does there seem a push or inclination to change that status. Yet the informal economy negatively affects membership and investment in the trade unions. Laborers who might be formally employed and join a union for protection may choose to branch out on their own instead. As a result, trade unions are inclined to oppose the informal sector, highlighting the costs and disadvantages of the system. Producers in the formal sector can similarly feel threatened by the informal economy. The flexibility of production, low labor and production costs, and bureaucratic freedom of the informal economy can be seen as consequential competition for formal producers, leading them to challenge and object to that sector. Last, the nature of the informal economy is largely anti-regulation and free of standard taxes, which diminishes the material and political power of government agents. Whatever the significance of these concerns are, the informal sector can shift political power and energies.

Poverty

The relationship between the informal sectors and poverty certainly is not simple nor does a clear, causal relationship exist. An inverse relationship between an increased informal sector and slower economic growth has been observed though. Average incomes are substantially lower in the informal economy and there is a higher preponderance of impoverished employees working in the informal sector. In addition, workers in the informal economy are less likely to benefit from employment benefits and social protection programs. For instance, a survey in Europe shows that the respondents who have difficulties to pay their household bills have worked informally more often in the past year than those that do not (10% versus 3% of the respondents).

Children and child labour

Children work in the informal economy in many parts of the world. They often work as scavengers (collecting recyclables from the streets and dump sites), day laborers, cleaners, construction workers, vendors, in seasonal activities, domestic workers, and in small workshops; and often work under hazardous and exploitative conditions. It is common for children to work as domestic servants in parts of Latin America and parts of Asia. Such children are very vulnerable to exploitation: often they are not allowed to take breaks or are required to work long hours; many suffer from a lack of access to education, which can contribute to social isolation and a lack of future opportunity. UNICEF considers domestic work to be among the lowest status, and reports that most child domestic workers are live-in workers and are under the round-the-clock control of their employers. Some estimates suggest that among girls, domestic work is the most common form of employment.

During times of economic crisis many families experience unemployment and job loss, thus compelling adolescents to supplement their parents’ income by selling goods or services to contribute to the family economy. At the core, youth must compromise their social activities with other youth, and instead prioritize their participation in the informal economy, thus manufacturing a labor class of adolescents who must take on an adult role within the family. Although it revolves around a negative stigma of deviance, for a majority of individuals, mostly people of color, the informal economy is not an ideal choice but a necessity for survival. Participating in the informal economy is becoming normalized due to the lack of resources available in low-income and marginalized communities, and no matter how hard they have to work, will not advance in the economic hierarchy. When a parent is either unemployed or their job is on low demand, they are compelled to find other methods to provide for themselves but most importantly their children. Yet, due to all the limitations and the lack of jobs, children eventually cooperate with their parent/s and also work for their family's economic well-being. By having to assist in providing for the family, children miss out on their childhood because instead of engaging in activities other youth their age participate in, they are obligated to take on an adult role, put the family first and contribute to the family's well-being.

The participation of adolescents in the informal economy, is a contentious issue due to the restrictions and laws in place for youth have to work. One of the main dilemmas that arise when children engage in this type of work, is that privileged adults, denounce children participation as forced labor. Due to the participant being young, the adults are viewed as “bad” parents because first they cannot provide for their children, second they are stripping the child from a “normal” childhood, and third, child labor is frowned upon. Furthermore, certain people believe that children should not be working because children do not know the risks and the pressure of working and having so much responsibility, but the reality is that for most families, the children are not being forced to work, rather they choose to help sustain their family’s income. The youth become forced by their circumstances, meaning that because of their conditions, they do not have much of a choice. Youth have the capability to acknowledge their family’s financial limitations and many feel that it is their moral obligation to contribute to the family income. Thus, they end up working without asking for an allowance or wage, because kids recognize that their parents cannot bring home enough income alone, thus their contribution is necessary and their involvement becomes instrumental for their family's economic survival.

Emir Estrada and Pierrette Hondagneu-Sotelo have gone to predominantly Latino communities of Los Angeles, CA. to observe the daily actions of street vendors. They analyze why adults participate in the informal economy. Although it revolves around a negative stigma of deviance, for a majority of individuals, the informal economy is not an ideal choice but an action necessary for survival. While witnessing the constant struggle of Latino individuals to make ends meet and trying to earn money to put food on the table, they witnessed how the participation of children either benefits the family or even hurt it. Through field notes derived from their participation, Estrada states, “children are not the ‘baggage’ that adult immigrants simply bring along. In the case of street vendors, we see that they are also contributors to family processes”. Estrada's findings demonstrate that children are working in order to help contribute to their household income, but most importantly, they play a vital role when it comes to language barriers. The kids are not simply workers, they achieve an understanding of how to manage a business and commerce.

Expansion and growth

The division of the economy into formal and informal sectors has a long heritage. Arthur Lewis in his seminal work Economic Development with Unlimited Supply of Labour, published in the 1950s, was the celebrated paradigm of development for the newly independent countries in the 1950s and 1960s. The model assumed that the unorganized sector with the surplus labour will gradually disappear as the surplus labour gets absorbed in the organised sector. The Lewis model is drawn from the experience of capitalist countries in which the share of agriculture and unorganized sector showed a spectacular decline, but it didn't prove to be true in many developing countries, including India. On the other hand, probabilistic migration models developed by Harris and Todaro in the 1970s envisaged the phenomenon of the informal sector as a transitional phase through which migrants move to the urban centers before shifting to formal sector employment. Hence it is not a surprise to see policy invisibility in the informal sector. Curiously, the informal sector does not find a permanent place in the Marxian theory since they anticipate the destruction of the pre-capitalist structure as a result of the aggressive growth of capitalism. To them, in the course of development, 'the small fish is being eaten by the big fish'. Therefore, neither in the Marxian theory nor in the classical economic theory, the unorganized sector holds a permanent place in the economic literature.

The informal sector has been expanding as more economies have started to liberalize. This pattern of expansion began in the 1960s when a lot of developing countries didn't create enough formal jobs in their economic development plans, which led to the formation of an informal sector that didn't solely include marginal work and actually contained profitable opportunities. In the 1980s, the sector grew alongside formal industrial sectors. In the 1990s, an increase in global communication and competition led to a restructuring of production and distribution, often relying more heavily on the informal sector.

Over the past decade, the informal economy is said to account for more than half of the newly created jobs in Latin America. In Africa it accounts for around eighty percent. Many explanations exist as to why the informal sector has been expanding in the developing world throughout the past few decades. It is possible that the kind of development that has been occurring has failed to support the increased labor force in a formal manner. Expansion can also be explained by the increased subcontracting due to globalization and economic liberalization. Finally, employers could be turning toward the informal sector to lower costs and cope with increased competition.

Such extreme competition between industrial countries occurred after the expansion of the EC to markets of the then new member countries Greece, Spain and Portugal, and particularly after the establishment of the Single European Market (1993, Treaty of Maastricht). Mainly for French and German corporations it led to systematic increase of their informal sectors under liberalized tax laws, thus fostering their mutual competitiveness and against small local competitors. The continuous systematic increase of the German informal sector was stopped only after the establishment of the EURO and the execution of the Summer Olympic Games 2004, which has been the first and (up to now) only in the Single Market. Since then the German informal sector stabilized on the achieved 350 bn € level which signifies an extremely high tax evasion for a country with 90% salary-employment.

According to the Swedish International Development Cooperation Agency (SIDA), the key drivers for the growth of the informal economy in the twenty-first century include:

limited absorption of labour, particularly in countries with high rates of population or urbanisation
excessive cost and regulatory barriers of entry into the formal economy, often motivated by corruption
weak institutions, limiting education and training opportunities as well as infrastructure development
increasing demand for low-cost goods and services
migration motivated by economic hardship and poverty
difficulties faced by women in gaining formal employment

Historically, development theories have asserted that as economies mature and develop, economic activity will shift from the informal to the formal sphere. In fact, much of the economic development discourse is centered around the notion that formalization indicates how developed a country's economy is; for more on this discussion see the page on fiscal capacity. However, evidence suggests that the progression from informal to formal sectors is not universally applicable. While the characteristics of a formalized economy – full employment and an extensive welfare system – have served as effective methods of organizing work and welfare for some nations, such a structure is not necessarily inevitable or ideal. Indeed, development appears to be heterogeneous in different localities, regions, and nations, as well as the type of work practiced. For example, at one end of the spectrum of the type of work practiced in the informal economy are small-scale businesses and manufacturing; on the other "street vendors, shoe shiners, junk collectors and domestic servants." Regardless of how the informal economy develops, its continued growth that it cannot be considered a temporary phenomenon.

Policy suggestions

Informal beverage vendor in Guatemala City

As it has been historically stigmatized, policy perspectives viewed the informal sector as disruptive to the national economy and a hindrance to development. The justifications for such criticisms include viewing the informal economy as a fraudulent activity that results in a loss of revenue from taxes, weakens unions, creates unfair competition, leads to a loss of regulatory control on the government's part, reduces observance of health and safety standards, and reduces the availability of employment benefits and rights. These characteristics have led to many nations pursuing a policy of deterrence with strict regulation and punitive procedures.

In a 2004 report, the Department for Infrastructure and Economic Cooperation under SIDA explained three perspectives on the role of government and policy in relation to the informal economy.

Markets function efficiently on their own; government interference would only lead to inefficiency and dysfunction.
The informal economy functions outside of government control, largely because those who participate wish to avoid regulation and taxation.
The informal economy is enduring; suitable regulation and policies are required.

As informal economy has significant job creation and income generation potential, as well as the capacity to meet the needs of poor consumers by providing cheaper and more accessible goods and services, many stakeholders subscribe to the third perspective and support government intervention and accommodation. Embedded in the third perspective is the significant expectation that governments will revise policies that have favored the formal sphere at the expense of the informal sector.

Theories of how to accommodate the informal economy argue for government policies that, recognizing the value and importance of the informal sector, regulate and restrict when necessary but generally work to improve working conditions and increase efficiency and production.

The challenge for policy interventions is that so many different types of informal work exist; a solution would have to provide for a diverse range of circumstances. A possible strategy would be to provide better protections and benefits to informal sector players. However, such programs could lead to a disconnect between the labor market and protections, which would not actually improve informal employment conditions. In a 2014 report monitoring street vending, WIEGO suggested urban planners and local economic development strategists study the carrying capacity of areas regularly used by informal workers and deliver the urban infrastructure necessary to support the informal economy, including running water and toilets, street lights and regular electricity, and adequate shelter and storage facilities. That study also called for basic legal rights and protections for informal workers, such as appropriate licensing and permit practices.

An ongoing policy debate considers the value of government tax breaks for household services such as cleaning, babysitting and home maintenance, with an aim to reduce the shadow economy's impact. There are currently systems in place in Sweden and France which offer 50 percent tax breaks for home cleaning services. There has also been debate in the UK about introducing a similar scheme, with potentially large savings for middle-class families and greater incentive for women to return to work after having children. The European Union has used political measures to try to curb the shadow economy. Although no definitive solution has been established, the EU council has led dialogue on a platform that would combat undeclared work.

The World Bank's 2019 World Development Report on The Changing Nature of Work discusses the extension of social assistance and insurance schemes to informal workers given that, in 2018, 8 in 10 people in developing countries still receive no social assistance and 6 in 10 work informally.

Asia-Pacific

The International Labour Organization mentioned that in most developing nations located in the Asia-Pacific, the informal sector comprises a significant and vital percentage of the labor force. This sector constitutes around 60 percent of the labor force. Informal economy includes economic activities of laborers (legally and in practice) which are not or inadequately covered by official employment contracts or agreements. Informal employment means payment of wagers may not be guaranteed and retrenchment can be implemented without prior notice or compensation from employers. There are generally substandard health and safety conditions as well as nonexistence of social benefits which include sick pay, pension, and health coverage. The informal economy absorbs a larger part of the ever-growing workforce in urban hubs. In 2015, urban populations of Asian countries started to grow while the service sector also continued to increase. These developments contributed to the extensive expansion of urban informal economy in practically all of Asia.

In India, the country’s informal sector accounted for over 80 percent of the non-agricultural industry during the last 20 years. Inadequate employment denotes the option for majority of India’s citizens is to find work in the informal sector which continues to grow because of the contract system and outsourcing of production. An article in First Post (June 2018) said approximately 1.3 billion people or more than 68 percent of employed persons in the Asia-Pacific earn through the informal economy. It is prevalent in the countryside (around 85 percent) and almost 48 percent in urban locations. 2 billion of the global population (61 percent) works in the informal sector. According to an article published in Eco-Business in June 2018, the informal sector has emerged as an essential component of the economic environment of cities in this region. Henceforth, the importance of contribution of informal workers deserves recognition.

Life-cycle assessment

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Life-cycle_assessment

Illustration of the general phases of a life cycle assessment, as described by ISO 14040

Life cycle assessment or LCA (also known as life cycle analysis) is a methodology for assessing environmental impacts associated with all the stages of the life cycle of a commercial product, process, or service. For instance, in the case of a manufactured product, environmental impacts are assessed from raw material extraction and processing (cradle), through the product's manufacture, distribution and use, to the recycling or final disposal of the materials composing it (grave).

An LCA study involves a thorough inventory of the energy and materials that are required across the industry value chain of the product, process or service, and calculates the corresponding emissions to the environment. LCA thus assesses cumulative potential environmental impacts. The aim is to document and improve the overall environmental profile of the product.

Widely recognized procedures for conducting LCAs are included in the 14000 series of environmental management standards of the International Organization for Standardization (ISO), in particular, in ISO 14040 and ISO 14044. ISO 14040 provides the 'principles and framework' of the Standard, while ISO 14044 provides an outline of the 'requirements and guidelines'. Generally, ISO 14040 was written for a managerial audience and ISO 14044 for practitioners. As part of the introductory section of ISO 14040, LCA has been defined as the following:

LCA studies the environmental aspects and potential impacts throughout a product's life cycle (i.e., cradle-to-grave) from raw materials acquisition through production, use and disposal. The general categories of environmental impacts needing consideration include resource use, human health, and ecological consequences.

Criticisms have been leveled against the LCA approach, both in general and with regard to specific cases (e.g., in the consistency of the methodology, particularly with regard to system boundaries, and the susceptibility of particular LCAs to practitioner bias with regard to the decisions that they seek to inform). Without a formal set of requirements and guidelines, an LCA can be completed based on a practitioner's views and believed methodologies. In turn, an LCA completed by 10 different parties could yield 10 different results. The ISO LCA Standard aims to normalize this; however, the guidelines are not overly restrictive and 10 different answers may still be generated.

Definition, synonyms, goals, and purpose

Life cycle assessment (LCA) is sometimes referred to synonymously as life cycle analysis in the scholarly and agency report literatures. Also, due to the general nature of an LCA study of examining the life cycle impacts from raw material extraction (cradle) through disposal (grave), it is sometimes referred to as "cradle-to-grave analysis".

As stated by the National Risk Management Research Laboratory of the EPA, "LCA is a technique to assess the environmental aspects and potential impacts associated with a product, process, or service, by:

Compiling an inventory of relevant energy and material inputs and environmental releases
Evaluating the potential environmental impacts associated with identified inputs and releases
Interpreting the results to help you make a more informed decision".

Hence, it is a technique to assess environmental impacts associated with all the stages of a product's life from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling. The results are used to help decision-makers select products or processes that result in the least impact to the environment by considering an entire product system and avoiding sub-optimization that could occur if only a single process were used.

Therefore, the goal of LCA is to compare the full range of environmental effects assignable to products and services by quantifying all inputs and outputs of material flows and assessing how these material flows affect the environment. This information is used to improve processes, support policy and provide a sound basis for informed decisions.

The term life cycle refers to the notion that a fair, holistic assessment requires the assessment of raw-material production, manufacture, distribution, use and disposal including all intervening transportation steps necessary or caused by the product's existence.

Despite attempts to standardize LCA, results from different LCAs are often contradictory, therefore it is unrealistic to expect these results to be unique and objective. Thus, it should not be considered as such, but rather as a family of methods attempting to quantify results through a different point-of-view. Among these methods are two main types: Attributional LCA and Consequential LCA. Attributional LCAs seek to attribute the burdens associated with the production and use of a product, or with a specific service or process, for an identified temporal period. Consequential LCAs seek to identify the environmental consequences of a decision or a proposed change in a system under study, and thus are oriented to the future and require that market and economic implications must be taken into account. In other words, Attributional LCA "attempts to answer 'how are things (i.e. pollutants, resources, and exchanges among processes) flowing within the chosen temporal window?', while Consequential LCA attempts to answer 'how will flows beyond the immediate system change in response to decisions?"

A third type of LCA, termed "social LCA", is also under development and is a distinct approach to that is intended to assess potential social and socio-economic implications and impacts. Social Life Cycle Assessment (SLCA) is a useful tool for companies to identify and assess potential social impacts along the lifecycle of a product or service on various stakeholders (for example: workers, local communities, consumers). SLCA is framed by the UNEP/SETAC’s Guidelines for social life cycle assessment of products published in 2009 in Quebec. The tool builds on the ISO 26000:2010 Guidelines for Social Responsibility and the Global Reporting Initiative (GRI) Guidelines.

The limitations of LCA to focus solely on the ecological aspects of sustainability, and not the economical or social aspects, distinguishes it from product line analysis (PLA) and similar methods. This limitation was made deliberately to avoid method overload but recognizes these factors should not be ignored when making product decisions.

Some widely recognized procedures for LCA are included in the ISO 14000 series of environmental management standards, in particular, ISO 14040 and 14044. Greenhouse gas (GHG) product life cycle assessments can also comply with specifications such as Publicly Available Specification (PAS) 2050 and the GHG Protocol Life Cycle Accounting and Reporting Standard.

Main ISO phases of LCA

According to standards in the ISO 14040 and 14044, an LCA is carried out in four distinct phases, as illustrated in the figure shown at the above right (at opening of the article). The phases are often interdependent, in that the results of one phase will inform how other phases are completed. Therefore, none of the stages should be considered finalized until the entire study is complete.

Goal and Scope

The ISO LCA Standard requires a series of parameters to be quantitatively and qualitatively expressed, which are occasionally referred to as study design parameters (SPDs). The two main SPDs for an LCA are the Goal and Scope, both which must be explicitly stated. It is recommended that a study uses the keywords represented in the Standard when documenting these details (e.g., "The goal of the study is...") to make sure there is no confusion and ensure the study is being interpreted for its intended use.

Generally, an LCA study begins with an explicit statement of the goal, which sets out the context of the study and explains how and to whom the results are to be communicated. Per ISO guidelines, the goal must unambiguously state the following items:

The intended application
Reasons for carrying out the study
The audience
Whether the results will be used in a comparative assertion released publicly

The goal should also be defined with the commissioner for the study, and it is recommended a detailed description for why the study is being carried out is acquired from the commissioner.

Following the goal, the scope must be defined by outlining the qualitative and quantitative information included in the study. Unlike the goal, which may only include a few sentences, the scope often requires multiple pages. It is set to describe the detail and depth of the study and demonstrate that the goal can be achieved within the stated limitations. Under the ISO LCA Standard guidelines, the scope of the study should outline the following:

Product System, which is a collection of processes (activities that transform inputs to outputs) that are needed to perform a specified function and are within the system boundary of the study. It is representative of all the processes in the life cycle of a product or process.
Functional Unit, which defines precisely what is being studied, quantifies the service delivered by the system, provides a reference to which the inputs and outputs can be related, and provides a basis for comparing/analyzing alternative goods or services. The functional unit is a very important component of LCA and needs to be clearly defined. It is used as a basis for selecting one or more product systems that can provide the function. Therefore, the functional unit enables different systems to be treated as functionally equivalent. The defined functional unit should be quantifiable, include units, consider temporal coverage, and not contain product system inputs and outputs (e.g., kg CO₂ emissions). Another way to look at it is by considering the following questions:
1. What?
2. How much?
3. For how long / how many times?
4. Where?
5. How well?
Reference Flow, which is the amount of product or energy that is needed to realize the functional unit. Typically, the reference flow is different qualitatively and quantitatively for different products or systems across the same reference flow; however, there are instances where they can be the same.
System Boundary, which delimits which processes should be included in the analysis of a product system, including whether the system produces any co-products that must be accounted for by system expansion or allocation. The system boundary should be in accordance with the stated goal of the study.
Assumptions and Limitations, which includes any assumptions or decisions made throughout the study that may influence the final results. It is important these are made transmitted as the omittance may result in misinterpretation of the results. Additional assumptions and limitations necessary to accomplish the project are often made throughout the project and should recorded as necessary.
Data Quality Requirements, which specify the kinds of data that will be included and what restrictions. According to ISO 14044, the following data quality considerations should be documented in the scope:
1. Temporal Coverage
2. Geographical Coverage
3. Technological Coverage
4. Precision, completeness, and representativeness of the data
5. Consistency and reproducibility of the methods used in the study
6. Sources of Data
7. Uncertainty of information and any recognized data gaps
Allocation Procedure, which is used to partition the inputs and outputs of a product and is necessary for processes that produce multiple products, or co-products. This is also known as multifunctionality of a product system. ISO 14044 presents a hierarchy of solutions to deal with multifunctionality issues, as the choice of allocation method for co-products can significantly impact results of an LCA. The hierarchy methods are as follows:
1. Avoid Allocation by Sub-Division - this method attempts to disaggregate the unit process into smaller sub-processes in order to separate the production of the product from the production of the co-product.
2. Avoid Allocation through System Expansion (or substitution) - this method attempts to expand the process of the co-product with the most likely way of providing the secondary function of the determining product (or reference product). In other words, by expanding the system of the co-product in the most likely alternative way of producing the co-product independently (System 2). The impacts resulting from the alternative way of producing the co-product (System 2) are then subtracted from the determining product to isolate the impacts in System 1.
3. Allocation (or partition) based on Physical Relationship - this method attempts to divide inputs and outputs and allocate them based on physical relationships between the products (e.g., mass, energy-use, etc.).
4. Allocation (or partition) based on Other Relationship (non-physical) - this method attempts to divide inputs and outputs and allocate them based on non-physical relationships (e.g., economic value).
Impact Assessment, which includes an outline of the impact categories identified under interest for the study, and the selected methodology used to calculate the respective impacts. Specifically, life cycle inventory data is translated into environmental impact scores, which might include such categories as human toxicity, smog, global warming, and eutrophication. As part of the scope, only an overview needs to be provided, as the main analysis on the impact categories is discussed in the Life Cycle Impact Assessment (LCIA) phase of the study.
Documentation of Data, which is the explicit documentation of the inputs/outputs (individual flows) used within the study. This is necessary as most analyses do not consider all inputs and outputs of a product system, so this provides the audience with a transparent representation of the selected data. It also provides transparency for why the system boundary, product system, functional unit, etc. was chosen.

Life Cycle Inventory (LCI)

Life Cycle Inventory (LCI) analysis involves creating an inventory of flows from and to nature (ecosphere) for a product system. It is the process of quantifying raw material and energy requirements, atmospheric emissions, land emissions, water emissions, resource uses, and other releases over the life cycle of a product or process. In other words, it is the aggregation of all elementary flows related to each unit process within a product system.

To develop the inventory, it is often recommended to start with a flow model of the technical system using data on inputs and outputs of the product system.The flow model is typically illustrated with a flow diagram that includes the activities that are going to be assessed in the relevant supply chain and gives a clear picture of the technical system boundaries. Generally, the more detailed and complex the flow diagram, the more accurate the study and results. The input and output data needed for the construction of the model is collected for all activities within the system boundary, including from the supply chain (referred to as inputs from the technosphere).

According to ISO 14044, an LCI should be documented using the following steps:

Preparation of data collection based on goal and scope
Data Collection
Data Validation (even if using another work's data)
Data Allocation (if needed)
Relating Data to the Unit Process
Relating Data to the Functional Unit
Data Aggregation

As referenced in the ISO 14044 standard, the data must be related to the functional unit, as well as the goal and scope. However, since the LCA stages are iterative in nature, the data collection phase may cause the goal or scope to change. Conversely, a change in the goal or scope during the course of the study may cause additional collection of data or removal or previously collected data in the LCI.

The output of an LCI is a compiled inventory of elementary flows from all of the processes in the studied product system(s). The data is typically detailed in charts and requires a structured approach due to its complex nature.

When collecting the data for each process within the system boundary, the ISO LCA standard requires the study to measure or estimate the data in order to quantitatively represent each process in the product system. Ideally, when collecting data, a practitioner should aim to collect data from primary sources (e.g., measuring inputs and outputs of a process on-site or other physical means). Questionnaire are frequently used to collect data on-site and can even be issued to the respective manufacturer or company to complete. Items on the questionnaire to be recorded may include:

Product for Data Collection
Data Collector and Date
Period of Data Collection
Detailed Explanation of the Process
Inputs (raw materials, ancillary materials, energy, transportation)
Outputs (emissions to air, water, and land)
Quantity and Quality of each input and output

Oftentimes, the collection of primary data may be difficult and deemed proprietary or confidential by the owner. An alternative to primary data is secondary data, which is data that comes from LCA databases, literature sources, and other past studies. With secondary sources, it is often you find data that is similar to a process but not exact (e.g., data from a different country, slightly different process, similar but different machine, etc.). As such, it is important to explicitly document the differences in such data. However, secondary data is not always inferior to primary data. For example, referencing another work's data in which the author used very accurate primary data. Along with primary data, secondary data should document the source, reliability, and temporal, geographical, and technological representativeness.

When identifying the inputs and outputs to document for each unit process within the product system of an LCI, a practitioner may come across the instance where a process has multiple input streams or generate multiple output streams. In such case, the practitioner should allocate the flows based on the "Allocation Procedure" outlined in the previous "Goal and Scope" section of this article.

The technosphere is more simply defined as the human-made world, and considered by geologists as secondary resources, these resources are in theory 100% recyclable; however, in a practical sense, the primary goal is salvage. For an LCI, these technosphere products (supply chain products) are those that have been produced by humans, including products such as forestry, materials, and energy flows. Typically, they will not have access to data concerning inputs and outputs for previous production processes of the product. The entity undertaking the LCA must then turn to secondary sources if it does not already have that data from its own previous studies. National databases or data sets that come with LCA-practitioner tools, or that can be readily accessed, are the usual sources for that information. Care must then be taken to ensure that the secondary data source properly reflects regional or national conditions.

LCI methods include "process-based LCAs", economic input–output LCA (EIOLCA), and hybrid approaches. Process-based LCA is a bottom-up LCI approach the constructs an LCI using knowledge about industrial processes within the life cycle of a product, and the physical flows connecting them. EIOLCA is a top-down approach to LCI and uses information on elementary flows associated with one unit of economic activity across different sectors. This information is typically pulled from government agency national statistics tracking trade and services between sectors. Hybrid LCA is a combination of process-based LCA and EIOLCA.

The quality of LCI data is typically evaluated with the use of a pedigree matrix. Different pedigree matrices are available, but all contain a number of data quality indicators and a set of qualitative criteria per indicator. There is another hybrid approach integrates the widely used, semi-quantitative approach that uses a pedigree matrix, into a qualitative analysis to better illustrate the quality of LCI data for non-technical audiences, in particular policymakers.

Life Cycle Impact Assessment (LCIA)

Life Cycle Inventory analysis is followed by a life cycle impact assessment (LCIA). This phase of LCA is aimed at evaluating the potential environmental and human health impacts resulting from the elementary flows determined in the LCI. The ISO 14040 and 14044 standards require the following mandatory steps for completing an LCIA:

Mandatory

Selection of impaction categories, category indicators, and characterization models. The ISO Standard requires that a study selects multiple impacts that encompass "a comprehensive set of environmental issues". The impacts should be relevant to the geographical region of the study and justification for each chosen impact should be discussed. Often times in practice, this is completed by choosing an already existing LCIA method (e.g., TRACI, ReCiPe, AWARE, etc.).
Classification of inventory results. In this step, the LCI results are assigned to the chosen impact categories based on their known environmental effects. In practice, this is often completed using LCI databases or LCA software. Common impact categories include Global Warming, Ozone Depletion, Acidification, Human Toxicity, etc.
Characterization, which quantitatively transforms the LCI results within each impact category via "characterization factors" (also referred to as equivalency factors) to create "impact category indicators." In other words, this step is aimed at answering "how much does each result contribute to the impact category?" A main purpose of this step is to convert all classified flows for an impact into common units for comparison. For example, for Global Warming Potential, the unit is generally defined as CO₂-equiv or CO₂-e (CO₂ equivalents) where CO₂ is given a value of 1 and all other units are converted respective to their related impact.

In many LCAs, characterization concludes the LCIA analysis, as it is the last compulsory stage according to ISO 14044. However, the ISO Standard provides the following optional steps to be taken in addition to the aforementioned mandatory steps:

Optional

Normalization of results. This step aims to answer "Is that a lot?" by expressing the LCIA results in respect to a chosen reference system. A separate reference value is often chosen for each impact category, and the rationale for the step is to provide temporal and spatial perspective and to help validate the LCIA results. Standard references are typical impacts per impact category per: geographical zone, inhabitant of geographical zone (per person), industrial sector, or another product system or baseline reference scenario.
Grouping of LCIA results. This step is accomplished by sorting or ranking the LCIA results (either characterized or normalized depending on the prior steps chosen) into a single group or several groups as defined within the goal and scope. However, grouping is subjective and may be inconsistent across studies.
Weighting of impact categories. This step aims to determine the significance of each category and how important it is relative to the others. It allows studies to aggregate impact scores into a single indicator for comparison. Weighting is highly subjective and as it is often decided based on the interested parties' ethics. There are three main categories of weighting methods: the panel method, monetization method, and target method. ISO 14044 generally advises against weighting, stating that "weighting, shall not be used in LCA studies intended to be used in comparative assertions intended to be disclosed to the public". If a study decides to weight results, then the weighted results should always be reported together with the non-weighted results for transparency.

Life cycle impacts can also be categorized under the several phases of the development, production, use, and disposal of a product. Broadly speaking, these impacts can be divided into first impacts, use impacts, and end of life impacts. First impacts include extraction of raw materials, manufacturing (conversion of raw materials into a product), transportation of the product to a market or site, construction/installation, and the beginning of the use or occupancy. Use impacts include physical impacts of operating the product or facility (such as energy, water, etc.), and any maintenance, renovation, or repairs that are required to continue to use the product or facility. End of life impacts include demolition and processing of waste or recyclable materials.

Interpretation

Life cycle interpretation is a systematic technique to identify, quantify, check, and evaluate information from the results of the life cycle inventory and/or the life cycle impact assessment. The results from the inventory analysis and impact assessment are summarized during the interpretation phase. The outcome of the interpretation phase is a set of conclusions and recommendations for the study. According to ISO 14043, the interpretation should include the following:

Identification of significant issues based on the results of the LCI and LCIA phases of an LCA
Evaluation of the study considering completeness, sensitivity and consistency checks
Conclusions, limitations and recommendations

A key purpose of performing life cycle interpretation is to determine the level of confidence in the final results and communicate them in a fair, complete, and accurate manner. Interpreting the results of an LCA is not as simple as "3 is better than 2, therefore Alternative A is the best choice". Interpretation begins with understanding the accuracy of the results, and ensuring they meet the goal of the study. This is accomplished by identifying the data elements that contribute significantly to each impact category, evaluating the sensitivity of these significant data elements, assessing the completeness and consistency of the study, and drawing conclusions and recommendations based on a clear understanding of how the LCA was conducted and the results were developed.

Specifically, as voiced by M.A. Curran, the goal of the LCA interpretation phase is to identify the alternative that has the least cradle-to-grave environmental negative impact on land, sea, and air resources.

LCA uses

LCA was primarily used as a comparison tool, providing informative information on the environmental impacts of a product and comparing it to available alternatives. Its potential applications expanded to include marketing, product design, product development, strategic planning, consumer education, ecolabeling and government policy.

ISO specifies three types of classification in regard to standards and environmental labels:

Type I environmental labelling requires a third-party certification process to verify a products compliance against a set of criteria, according to ISO 14024.
Type II environmental labels are self-declared environmental claims, according to ISO 14021.
Type III environmental declaration, also known as environmental product declaration (EPD), uses LCA as a tool to report the environmental performance of a product, while conforming to the ISO standards 14040 and 14044.

EPDs provide a level of transparency that is being increasingly demanded through policies and standards around the world. They are used in the built environment as a tool for experts in the industry to compose whole building life cycle assessments more easily, as the environmental impact of individual products are known.

Data analysis

A life cycle analysis is only as accurate and valid as is its basis set of data. There are two fundamental types of LCA data–unit process data, and environmental input-output (EIO) data. A unit process data collects data around a single industrial activity and its product(s), including resources used from the environment and other industries, as well as its generated emissions throughout its life cycle. EIO data are based on national economic input-output data.

In 2001, ISO published a technical specification on data documentation, describing the format for life cycle inventory data (ISO 14048). The format includes three areas: process, modeling and validation, and administrative information.

When comparing LCAs, the data used in each LCA should be of equivalent quality, since no just comparison can be done if one product has a much higher availability of accurate and valid data, as compared to another product which has lower availability of such data.

Moreover, time horizon is a sensitive parameter and was shown to introduce inadvertent bias by providing one perspective on the outcome of LCA, when comparing the toxicity potential between petrochemicals and biopolymers for instance. Therefore, conducting sensitivity analysis in LCA are important to determine which parameters considerably impact the results, and can also be used to identify which parameters cause uncertainties.

Data sources used in LCAs are typically large databases. Common data sources include:

soca
EuGeos' 15804-IA
NEEDS
ecoinvent
PSILCA
ESU World Food
GaBi
ELCD
LC-Inventories.ch
Social Hotspots
ProBas
bioenergiedat
Agribalyse
USDA
Ökobaudat
Agri-footprint
Comprehensive Environmental Data Archive (CEDA)

As noted above, the inventory in the LCA usually considers a number of stages including materials extraction, processing and manufacturing, product use, and product disposal. When an LCA is done on a product across all stages, the stage with the highest environmental impact can be determined and altered. For example, woolen-garment was evaluated on its environmental impacts during its production, use and end-of-life, and identified the contribution of fossil fuel energy to be dominated by wool processing and GHG emissions to be dominated by wool production. However, the most influential factor was the number of garment wear and length of garment lifetime, indicating that the consumer has the largest influence on this products' overall environmental impact.

Variants

Cradle-to-grave or life cycle assessment

Cradle-to-grave is the full Life Cycle Assessment from resource extraction ('cradle'), to manufacturing, usage, and maintenance, all the way through to its disposal phase ('grave'). For example, trees produce paper, which can be recycled into low-energy production cellulose (fiberised paper) insulation, then used as an energy-saving device in the ceiling of a home for 40 years, saving 2,000 times the fossil-fuel energy used in its production. After 40 years the cellulose fibers are replaced and the old fibers are disposed of, possibly incinerated. All inputs and outputs are considered for all the phases of the life cycle.

Cradle-to-gate

Cradle-to-gate is an assessment of a partial product life cycle from resource extraction (cradle) to the factory gate (i.e., before it is transported to the consumer). The use phase and disposal phase of the product are omitted in this case. Cradle-to-gate assessments are sometimes the basis for environmental product declarations (EPD) termed business-to-business EPDs. One of the significant uses of the cradle-to-gate approach compiles the life cycle inventory (LCI) using cradle-to-gate. This allows the LCA to collect all of the impacts leading up to resources being purchased by the facility. They can then add the steps involved in their transport to plant and manufacture process to more easily produce their own cradle-to-gate values for their products.

Cradle-to-cradle or closed loop production

Cradle-to-cradle is a specific kind of cradle-to-grave assessment, where the end-of-life disposal step for the product is a recycling process. It is a method used to minimize the environmental impact of products by employing sustainable production, operation, and disposal practices and aims to incorporate social responsibility into product development. From the recycling process originate new, identical products (e.g., asphalt pavement from discarded asphalt pavement, glass bottles from collected glass bottles), or different products (e.g., glass wool insulation from collected glass bottles).

Allocation of burden for products in open loop production systems presents considerable challenges for LCA. Various methods, such as the avoided burden approach have been proposed to deal with the issues involved.

Gate-to-gate

Gate-to-gate is a partial LCA looking at only one value-added process in the entire production chain. Gate-to-gate modules may also later be linked in their appropriate production chain to form a complete cradle-to-gate evaluation.

Well-to-wheel

Well-to-wheel (WtW) is the specific LCA used for transport fuels and vehicles. The analysis is often broken down into stages entitled "well-to-station", or "well-to-tank", and "station-to-wheel" or "tank-to-wheel", or "plug-to-wheel". The first stage, which incorporates the feedstock or fuel production and processing and fuel delivery or energy transmission, and is called the "upstream" stage, while the stage that deals with vehicle operation itself is sometimes called the "downstream" stage. The well-to-wheel analysis is commonly used to assess total energy consumption, or the energy conversion efficiency and emissions impact of marine vessels, aircraft and motor vehicles, including their carbon footprint, and the fuels used in each of these transport modes. WtW analysis is useful for reflecting the different efficiencies and emissions of energy technologies and fuels at both the upstream and downstream stages, giving a more complete picture of real emissions.

The well-to-wheel variant has a significant input on a model developed by the Argonne National Laboratory. The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model was developed to evaluate the impacts of new fuels and vehicle technologies. The model evaluates the impacts of fuel use using a well-to-wheel evaluation while a traditional cradle-to-grave approach is used to determine the impacts from the vehicle itself. The model reports energy use, greenhouse gas emissions, and six additional pollutants: volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen oxide (NOx), particulate matter with size smaller than 10 micrometer (PM10), particulate matter with size smaller than 2.5 micrometer (PM2.5), and sulfur oxides (SOx).

Quantitative values of greenhouse gas emissions calculated with the WTW or with the LCA method can differ, since the LCA is considering more emission sources. For example, while assessing the GHG emissions of a battery electric vehicle in comparison with a conventional internal combustion engine vehicle, the WTW (accounting only the GHG for manufacturing the fuels) concludes that an electric vehicle can save around 50–60% of GHG. On the other hand, using a hybrid LCA-WTW method, concludes that GHG emission savings are 10-13% lower than the WTW results, as the GHG due to the manufacturing and the end of life of the battery are also considered.

Economic input–output life cycle assessment

Economic input–output LCA (EIOLCA) involves use of aggregate sector-level data on how much environmental impact can be attributed to each sector of the economy and how much each sector purchases from other sectors. Such analysis can account for long chains (for example, building an automobile requires energy, but producing energy requires vehicles, and building those vehicles requires energy, etc.), which somewhat alleviates the scoping problem of process LCA; however, EIOLCA relies on sector-level averages that may or may not be representative of the specific subset of the sector relevant to a particular product and therefore is not suitable for evaluating the environmental impacts of products. Additionally, the translation of economic quantities into environmental impacts is not validated.

Ecologically based LCA

While a conventional LCA uses many of the same approaches and strategies as an Eco-LCA, the latter considers a much broader range of ecological impacts. It was designed to provide a guide to wise management of human activities by understanding the direct and indirect impacts on ecological resources and surrounding ecosystems. Developed by Ohio State University Center for resilience, Eco-LCA is a methodology that quantitatively takes into account regulating and supporting services during the life cycle of economic goods and products. In this approach services are categorized in four main groups: supporting, regulating, provisioning and cultural services.

Exergy-based LCA

Exergy of a system is the maximum useful work possible during a process that brings the system into equilibrium with a heat reservoir. Wall clearly states the relation between exergy analysis and resource accounting. This intuition confirmed by DeWulf and Sciubba lead to Exergo-economic accounting and to methods specifically dedicated to LCA such as Exergetic material input per unit of service (EMIPS). The concept of material input per unit of service (MIPS) is quantified in terms of the second law of thermodynamics, allowing the calculation of both resource input and service output in exergy terms. This exergetic material input per unit of service (EMIPS) has been elaborated for transport technology. The service not only takes into account the total mass to be transported and the total distance, but also the mass per single transport and the delivery time.

Life cycle energy analysis

Life cycle energy analysis (LCEA) is an approach in which all energy inputs to a product are accounted for, not only direct energy inputs during manufacture, but also all energy inputs needed to produce components, materials and services needed for the manufacturing process. With LCEA, the total life cycle energy input is established.

Energy production

It is recognized that much energy is lost in the production of energy commodities themselves, such as nuclear energy, photovoltaic electricity or high-quality petroleum products. Net energy content is the energy content of the product minus energy input used during extraction and conversion, directly or indirectly. A controversial early result of LCEA claimed that manufacturing solar cells requires more energy than can be recovered in using the solar cell. Although these results were true when solar cells were first manufactured, their efficiency increased greatly over the years. Currently, energy payback time of photovoltaic solar panels range from a few months to several years. Module recycling could further reduce the energy payback time to around one month. Another new concept that flows from life cycle assessments is energy cannibalism. Energy cannibalism refers to an effect where rapid growth of an entire energy-intensive industry creates a need for energy that uses (or cannibalizes) the energy of existing power plants. Thus, during rapid growth, the industry as a whole produces no energy because new energy is used to fuel the embodied energy of future power plants. Work has been undertaken in the UK to determine the life cycle energy (alongside full LCA) impacts of a number of renewable technologies.

Energy recovery

If materials are incinerated during the disposal process, the energy released during burning can be harnessed and used for electricity production. This provides a low-impact energy source, especially when compared with coal and natural gas. While incineration produces more greenhouse gas emissions than landfills, the waste plants are well-fitted with regulated pollution control equipment to minimize this negative impact. A study comparing energy consumption and greenhouse gas emissions from landfills (without energy recovery) against incineration (with energy recovery) found incineration to be superior in all cases except for when landfill gas is recovered for electricity production.

Criticism

Energy efficiency is arguably only one consideration in deciding which alternative process to employ, and should not be elevated as the only criterion for determining environmental acceptability. For example, a simple energy analysis does not take into account the renewability of energy flows or the toxicity of waste products. Incorporating "dynamic LCAs", e.g., with regard to renewable energy technologies—which use sensitivity analyses to project future improvements in renewable systems and their share of the power grid—may help mitigate this criticism.

In recent years, the literature on life cycle assessment of energy technology has begun to reflect the interactions between the current electrical grid and future energy technology. Some papers have focused on energy life cycle, while others have focused on carbon dioxide (CO₂) and other greenhouse gases. The essential critique given by these sources is that when considering energy technology, the growing nature of the power grid must be taken into consideration. If this is not done, a given class energy technology may emit more CO₂ over its lifetime than it initially thought it would mitigate, with this most well documented in wind energy's case.

A problem that arises when using the energy analysis method is that different energy forms—heat, electricity, chemical energy etc.—have inconsistent functional units, different quality, and different values. This is due to the fact that the first law of thermodynamics measures the change in internal energy, whereas the second law measures entropy increase. Approaches such as cost analysis or exergy may be used as the metric for LCA, instead of energy.

LCA dataset creation

There are structured systematic datasets of and for LCAs.

A 2022 dataset provided standardized calculated detailed environmental impacts of >57,000 food products in supermarkets, potentially e.g., informing consumers or policy. There also is at least one crowdsourced database for collecting LCA data for food products.

Datasets can also consist of options, activities, or approaches, rather than of products – for example one dataset assesses PET bottle waste management options in Bauru, Brazil. There are also LCA databases about buildings – complex products – which a 2014 study compared.

LCA dataset platforms

There are some initiatives to develop, integrate, populate, standardize, quality control, combine and maintain such datasets or LCAs – for example:

The goal of the LCA Digital Commons Project of the U.S. National Agricultural Library is "to develop a database and tool set intended to provide data for use in LCAs of food, biofuels, and a variety of other bioproducts".
The Global LCA Data Access network (GLAD) by the UN's Life Cycle Initiative is a "platform which allows to search, convert and download datasets from different life cycle assessment dataset providers".
The BONSAI project "aims to build a shared resource where the community can contribute to data generation, validation, and management decisions" for "product footprinting" with its first goal being "to produce an open dataset and an open source toolchain capable of supporting LCA calculations". With product footprints they refer to the goal of "reliable, unbiased sustainability information on products".

Dataset optimization

Datasets that are suboptimal in accuracy or have gaps can be, temporarily until the complete data is available or permanently, be patched or optimized by various methods such as mechanisms for "selection of a dataset that represents the missing dataset that leads in most cases to a much better approximation of environmental impacts than a dataset selected by default or by geographical proximity" or machine learning.

Integration in systems and systems theory

Life-cycle assessments can be integrated as routine processes of systems, as input for modeled future socio-economic pathways, or, more broadly, into a larger context (such as qualitative scenarios).

For example, a study estimated the environmental benefits of microbial protein or harm of beef within a future socio-economic pathway, showing substantial deforestation reduction (56%) and climate change mitigation if only 20% of per-capita beef was replaced by microbial protein by 2050.

Life-cycle assessments, including as product/technology analyses, can also be integrated in analyses of potentials, barriers and methods to shift or regulate consumption or production.

The life-cycle perspective also allows considering losses and lifetimes of rare goods and services in the economy. For example, the usespans of, often scarce, tech-critical metals were found to be short as of 2022. Such data could be combined with conventional life-cycle analyses, e.g., to enable life-cycle material/labor cost analyses and long-term economic viability or sustainable design. One study suggests that in LCAs, resource availability is, as of 2013, "evaluated by means of models based on depletion time, surplus energy, etc."

Broadly, various types of life-cycle assessments (or commissioning such) could be used in various ways in various types of societal decision-making, especially because financial markets of the economy typically do not consider life cycle impacts or induced societal problems in the future and present—the "externalities" to the contemporary economy.

Critiques

Life cycle assessment is a powerful tool for analyzing commensurable aspects of quantifiable systems. Not every factor, however, can be reduced to a number and inserted into a model. Rigid system boundaries make accounting for changes in the system difficult. This is sometimes referred to as the boundary critique to systems thinking. The accuracy and availability of data can also contribute to inaccuracy. For instance, data from generic processes may be based on averages, unrepresentative sampling, or outdated results. This is especially the case for the use and end of life phases in the LCA. Additionally, social implications of products are generally lacking in LCAs. Comparative life cycle analysis is often used to determine a better process or product to use. However, because of aspects like differing system boundaries, different statistical information, different product uses, etc., these studies can easily be swayed in favor of one product or process over another in one study and the opposite in another study based on varying parameters and different available data. There are guidelines to help reduce such conflicts in results but the method still provides a lot of room for the researcher to decide what is important, how the product is typically manufactured, and how it is typically used.

An in-depth review of 13 LCA studies of wood and paper products found a lack of consistency in the methods and assumptions used to track carbon during the product lifecycle. A wide variety of methods and assumptions were used, leading to different and potentially contrary conclusions—particularly with regard to carbon sequestration and methane generation in landfills and with carbon accounting during forest growth and product use.

Moreover, the fidelity of LCAs can vary substantially as various data may not be incorporated, especially in early versions: for example, LCAs that do not consider regional emission information can under-estimate the life cycle environmental impact.

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