Connecting global emissions to fundamental human needs and their satisfaction

Different theories of environmental sociology propose relationships between economic growth, environmental degradation and QOL. Modernization theories, including 'economic and ecological modernization', argue for the positive role of economic growth and consumption in achieving sustainability and improving QOL [12, 15, 16]. These theories rely on assumptions of neo-classical economics and thus predict a strong link between consumption and QOL, represented by the linear positive relationship shown in figure 1.

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In contrast, the 'treadmills of production' theory states that, due to its expansive nature, economic growth is in fundamental conflict with environmental protection [12, 16]. This theory predicts that modern nations reach a point of 'decreased social efficiency of natural resource utilization', where initial steep increases in QOL might correlate with increasing carbon footprint but reach a threshold of diminishing returns and eventually a steady state [17] (figure 1). After this threshold, each consumption unit generates more environmental damage and less welfare than it did at lower levels of development [12]. In some cases, QOL can even decline when increased consumption results in more harm than benefit [2, 12].

The theory of 'human ecology' considers a broader context, recognizing that QOL might also be affected by non-consumption factors [16] such as social dyanmics [18], relationships [19], health [3], climate conditions [12, 16], political factors [5, 16], etc (see [12, 16, 20]). In this case, changes in consumption do not necessarily predict changes in well-being, as shown by the 'non-relation' constant or scatter plots in figure 1. The supplementary information (SI1 is available online at stacks.iop.org/ERL/14/014002/mmedia) presents a summary of the trends and related concepts from other disciplines that link consumption and non-consumption to QOL.

Empirical findings of threshold and weak relations between QOL and consumption point to the opportunity of reducing impact without affecting the QOL in wealthy nations [7, 24–26]. Early evidence for the threshold pattern was demonstrated by the Easterlin Paradox [27], where consumption positively correlates with QOL but only up to a point and not over the long term [27, 28]. Further investigations argued for a trend of diminishing returns between QOL and consumption [29–31]. Nevertheless, both down-concave trends concede that additional consumption yields steeper benefits to the QOL of the poor, compared to the rich [19, 31]. Although studies confirm the Easterlin Paradox at different geographical scopes [12, 24, 32], they generally overlook using subjective life satisfaction as an adequate proxy for needs satisfaction [31, 33], and of using economic proxies for resource use (SI1 and SI5).

Sustainability-oriented studies further confirm threshold relationships between objective indicators of QOL, energy use [14, 25, 26, 34–36] or carbon footprint [12, 13, 24, 37–41]. The marginal benefit of additional CO₂ emissions, as measured by increased QOL, quickly decreases at a carbon footprint of around 3 tons CO₂ per capita (t CO₂/cap) [13, 37] and becomes indistinguishable from zero at values above 10 t CO₂/cap [13, 34, 37]. A QOL-CO₂ threshold has been reported for several indicators of QOL, including life expectancy [34, 37, 38, 41], infrastructure access [13, 35, 36], education [5, 24, 26] and the Index of Sustainable Economic Welfare [17, 42]. These findings signal opportunities for resource efficient development by directing resources to areas that have demonstrable social benefits [5, 8], such as child-rearing [4, 17], education [24, 43], access to energy [35], nutrition [13, 39] and sanitation [13]. However, most measures of environmental impact have been limited to national footprints [24, 37, 41] or consumption domains [9, 10].

Policymakers and the general public are eager for measures of progress in terms of societal outcomes rather than monetary inputs (e.g. healthy people rather than investments in the health sector) [5]. A multidimensional approach to the QOL-impact relationship considers the underlying human needs that enhance QOL [8] and whose satisfaction ultimately drives impact [14]. Apart from few exceptions [24], most studies measure QOL through single, composite, or broad indicators, such as life expectancy [36–38, 41], human development index [34], or life satisfaction [12, 27–30, 32], respectively. However, QOL not only depends on the level to which human needs are objectively met, but also on peoples' subjective satisfaction with respect to such levels [3, 8]. Initiatives such as the Better Life Index [44] or the Social Progress Index [45] demonstrate the complementarity of objective and subjective indicators for sounder policies [8].

We apply the framework of fundamental human needs to study the link between sustainability and QOL [2]. Max-Neef and colleagues recognized the bias of studying consumption and QOL based on consumption domains (e.g. transport, housing) [9, 23] rather than looking at their contribution to life domains (e.g. work, leisure, health) [3]. They proposed that all that we have and do, as well as the spaces in which we interact and the skills we build, are potential 'satisfiers' that contribute to QOL. In their view, QOL is a consequence of satisfying nine fundamental human needs: subsistence, protection, creation, identity, affection, participation, understanding, leisure and freedom [2]. These human needs are immutable across societies and throughout time. While other frameworks define universal saisfiers [46], Max-Neef argues that strategies to satisfy needs are entirely flexible and determined by each individual or group. Thus, satisfiers can be sustainable or unsustainable, based on different types of capital: natural, social and cultural [47].

We find this framework useful as it encompasses all the QOL-consumption relations described in figure 1 [2]. Further, the concept of satisfiers for needs is comprehensive, and inclusive of market and non-market goods. In contrast to similar frameworks [48], Max-Neef provides abundant examples that can be used as guidelines to model goods as satisfiers and to choose indicators of need satisfaction (SI table 3) [49]. Unlike the hierarchical taxonomy of Maslow [50], Max-Neef proposes a horizontal view of needs which is supported by robust research that reports needs to be fairly independent of each other [19]. For example, individuals with low material living standards can have better psychological and social well-being than their well-off counterparts [18, 19, 32, 51].

We take a multi-dimensional approach to QOL [3, 8] by applying the framework of fundamental human needs. As others before us, we model economic goods as satisfiers [47, 52] as a basis to estimate the energy and carbon footprints of fundamental human needs at a global and country level [53]. We then perform a cross-sectional analysis of 35 different objective and subjective indicators of needs satisfaction as a function of their footprints across 44 nations. To our knowledge, this is the first study to provide global and country-level estimates of the carbon and energy associated with fundamental human needs and their satisfaction.

First, we proposed a correspondence between the 200 economic goods available in the input–output database (EXIOBASE3-2007 [54, 55]) and the nine human needs [2] as shown in step 1 of table 1. Through group discussions, we discarded the most unlikely relationships between market goods and needs following Max-Neef's taxonomy and examples as guidelines [2, 49]. In the development of the correspondence matrix, we established conceptual identities between goods and needs to use as a guiding logic [2, 49] (see SI2 for details). As a result, subsistence relied heavily on food and housing, and to a lesser extent on transport and manufactured goods. Protection included health care, safety and financial security and can be satisfied by a range of goods, from insurances to heating fuels. Creation included the means to create and exercise creativity in both formal and informal work, as well as the application of art and crafts skills to material objects [56]. Freedom, understood as spatial and temporal plasticity, relied on market items that save time such as transport, domestic appliances and services (e.g. outsourcing of household work). Leisure included transport and energy for pleasure, as well as recreational services and entertainment. Identity relates mostly to goods that enable expression of preferences such as luxury items, clothing or diets. Participation related to communication devices, media and club memberships, while understanding associated to diverse pedagogic goods, from computers to educational services. Affection was not linked to any market good in the database and is therefore not included in this analysis.

A novelty of our model is to allow one market good to satisfy several needs simultaneously as 'synergistic satisfiers' [49]. For example, purchasing food directly satisfies subsistence but also identity, as reflected in diet and cuisine. We recognized that subsistence and protection are more directly reliant on material prerequisites compared to other needs (participation, identity, etc) [7, 24, 40]. Accordingly, we derived an allocation key based on the expenditure ratios between the lowest and highest income groups for each type of market good [40] by assuming that discretionary expenditure in synergistic basic goods aims to satisfy non-physical needs [40]. For example, if people in the lowest income quintiles spent on average 30 USD per capita on clothing, while the highest income quintiles spent 100 USD/cap, we allocated 30% of the total expenditure on clothing as a satisfier for subsistence whilst the remaining 70% went to identity. We used a US expenditure survey [57] to derive ratios and split synergistic satisfiers between basic needs (subsistence and protection) and other needs (step 2 table 1).

Finally, we conducted a Monte Carlo simulation to characterize the uncertainty of generalizing the allocation ratios from step 2 to the global economy. By testing all possible splits, we find the same relative hierarchy of the needs' carbon footprints and our estimates fall within the interquartile range of dispersion (see SI2). While the allocation values can certainly be refined by using country-specific data, our initial estimate proved to be robust and generalizable.

Consumption footprints consider all the energy and carbon emissions embodied in the production of goods, and attribute them to final consumers. In this sense, the carbon footprint of a nation equals the direct emissions occurring due to households' transport, heating and cooking, plus the embodied impact in the production of all consumed goods and services [53]. We model the final demand of households, governments, and non-profit institutions serving households for the year 2007, assuming that they all consume to satisfy societal needs.

We used the standard Leontief Input–Output model [58] to calculate energy and carbon footprints for 2007 based in EXIOBASE3, an open-access environmentally extended multiregional input–output database [55, 59] that captures the global economic activity and resources. We consider both combustion and non-combustion greenhouse gases (CO₂, CH₄, N₂O and SF₆) [55] normalized to carbon dioxide equivalents (CO₂eq) by using the IPCC 2007 characterization factors [55]. The net energy footprint includes the primary and secondary energy carriers used by industries for production of goods [55, 60]. Details about the footprints calculations are found in the SI3 [55, 60]. EXIOBASE3 covers the 44 largest economies, which make up 91% of global GDP and 65% of the world population. The rest of the world is represented by five regions of Middle East, America, Europe, Asia Pacific and Africa [55]. The global carbon and net energy embodied in consumption are used for the first section of results i.e. including the Rest of the World regions (figure 2). Embodied plus direct household energy and emissions were considered to compute footprints of needs across the 44 individual countries and assess need satisfaction (figures 3 and 4 and table 4). Finally, by applying the concept of consumption and footprint elasticity [9, 23], we compared marginal differences in consumption and footprints with respect to differences in the total consumption associated to needs (see SI3).

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Table 2 presents our dashboard of indicators, compiled under the following heuristics [8]: (1) QOL is multi-dimensional and should be measured in terms of specific human needs; (2) the evaluation of multiple needs should combine different scales: from individuals to societal level; (3) combining subjective and objective measures is necessary to identify the inputs that improve QOL. To guide our selection of indicators, we referred to Max-Neef's examples of satisfiers for the existential categories of 'being' and 'doing' [2, 49]. Detailed considerations and rationale for indicator choice are found in the supplementary material (see SI3 and SI table 1).

Subjective well-being indicators are self-reports that capture the percentage of individuals who are satisfied with respect to a need. When available, we included measures of values to represent the importance of a certain need for a population [61]. Objective indicators are assessed by a third party and used to represent infrastructure, social institutions, or health status [5, 8]. For example, to assess the subjective satisfaction of freedom we used the question: 'are you satisfied with freedom to choose what to do with your life?' [62]. To assess the importance of freedom, we used the Schwarz scale item: 'it is important to take own decisions. She/he likes to be free and not depend on others' [63]. To measure the objective status of freedom in a country, we took the measure of tolerance, inclusion, and personal rights reported in the Social Progress Index [45]. We compiled 35 objective and subjective country-level indicators from the databases (in table 2). When sensible, we prioritized single over composite indicators to prevent conceptual overlaps. However, objective indicators for freedom, democracy, and creativity do cover multiple dimensions. See the SI for the full referenced inventory of indicators for each need and the measure of satisfaction rates (appendix).

Using 'need satisfaction rate' as the dependent variable and the 'per capita carbon footprint of need' as the independent variable, we ran unweighted cross-country bivariate regressions to test the association between carbon footprint of needs and satisfaction outcomes (see SI 5). The mathematical forms of the models are, respectively:

$$\begin{eqnarray}&&{{Y}}_{ji}={\beta }_{o}+{\beta }_{1}C{F}_{ni}+{\upsilon }_{ni}\end{eqnarray} \tag{ 1 }$$

$$\begin{eqnarray}&&{{Y}}_{ji}={\beta }_{o}+{\beta }_{1}C{F}_{ni}+{\beta }_{2}C{F}_{ni}^{2}+{\upsilon }_{ni}\end{eqnarray} \tag{ 2 }$$

$$\begin{eqnarray}&&{{Y}}_{ji}={\beta }_{o}C{F}_{ni}^{{\beta }_{1}}+{\upsilon }_{ni},\end{eqnarray} \tag{ 3 }$$

where $Y$ is the reported satisfaction rate for each indicator j of each need i. CF is the per capita carbon footprint of each need i, in every nation n. The β coefficients are constants that result from the fit and $\upsilon$ is the error term. The cut-off criteria to accept a model fit between carbon footprint and need satisfaction is an adjusted R² above 0.28, while the criteria to accept a statistical significant relationship is set at 5% (p-value <0.05) for all the relationships investigated: linear, quadratic and power law. In similar studies, objective indicators often yield an R² above 0.5, while for subjective or social indicators, values lower than 0.25 are commonly accepted, given statistical significance [13, 24, 75]. Because we combine an assortment of indicator types and given our sample size (40 < N < 50), we establish our criteria seeking to discard weak evidence.

We hypothesize that linear curve fits support the aforementioned theory of 'ecological modernization' while nonlinear fits sustain the 'treadmills of production' theory. A significant power-law fit would imply diminishing returns on QOL. Quadratic fits might indicate saturating thresholds or even declining QOL, given a negative significant coefficient. Non-relationships might be explained by factors of human ecology [12]. However, we do not account explicitly for such factors and thus cannot confirm nor discard their role.

At a global level, subsistence drives 28% of global emissions followed by protection, freedom, identity, and creation (figure 2). While food is important, housing contributes the largest share of the carbon footprint of subsistence. Protection has the second highest carbon footprint with 21% of global emissions and the highest expenditure (see SI), in line with previous findings which trace 50% of impact to subsistence and protection [52]. Freedom and identity together make up around 27% of global emissions. Creation and leisure underlie around 21% of the total carbon emissions, while understanding and participation amount about 3% of the total carbon footprint. Figure 2 presents the linkages between human needs and the common categorization of goods by consumption domains (housing, services, mobility, etc). The supplementary data contains the expenditure and footprints of human needs for the 44 nations and 5 world regions.

Creation is the most intensive need with a world average of 2.2 kg CO₂ eq and 36 MJ per EUR of expenditure, followed by subsistence and leisure (figures 3(a), (b)). By contrast, understanding and protection are the least intensive, due to the large share of services that they require [9] (figure 2). The 22 poorest nations of our sample expend 2–4 times more carbon and energy per unit of consumption, compared to the 22 wealthiest (figures 3(a), (b)). However, the low intensity of wealthy nations is counteracted by their consumption volume, resulting in 2–7 times higher footprints, compared to the poorest nations, e.g. twice the carbon footprint for understanding, 4 times higher for subsistence and up to 7 times higher for protection and leisure (figure 3(c)). These trends point to the role of economic development in lowering the carbon intensity of human needs [9, 10]. However, it also signals that the benefits of more efficient technical systems and lower intensities are undermined by exacerbated consumption via the rebound effect [76, 77].

Brazil, Russia, India, China and South Africa (BRICS) have the lowest footprints per capita, but the highest impact intensities. Since 2.8 billon people inhabit the emergent economies of the BRICS group, the current footprint differences between wealthy nations and the BRICS (figure 3(c)) signals the potential for increased emissions in the coming decades. It is worth noting that all groups of nations show a similar distribution of carbon among needs, and only the magnitudes vary (figure 3(c)). Interestingly, this is not the case when looking at consumption categories (figure 2), where low-income nations tend to concentrate emissions in food and housing [9, 23].

We used elasticities to test the sensitivity of changes in consumption and footprints of needs with respect to changes in total expenditure (table 3). A 1% increases in total consumption corresponds to more than 1% increases in the consumption of most needs (ε > 1), except for subsistence, identity and freedom, which change at decreasing rates (ε < 1). Carbon and energy footprints both change at decreasing rates with respect to expenditure. However, the carbon footprint of needs is generally more sensitive to consumption changes [9]. Protection, leisure, participation and understanding are some of the most sensitive needs, as shown by a higher ε coefficient. On the contrary, identity is one of the least sensitive, as it is satisfied by a large share of food products (figure 2), which are basic goods [10].

When assessing needs satisfaction, we observe no universal pattern between the degree of satisfaction and the carbon emissions expended in those needs (figures 4(a), (b)). The threshold pattern found in previous studies [13, 24, 27, 28, 34, 36, 37] is confirmed for 5 indicators; 11 of which are objective, 2 value indicators and 2 subjective well-being. For 20 out of 35 relationships investigated, we find no correlation between the carbon footprint of human needs and their satisfaction. The diversity of relationships becomes evident when exploring figures 4(a), (b). Table 4 summarizes the model fits for all 35 tested relationships. The adjusted R² indicates how well the carbon footprint predicts needs satisfaction i.e. the strength of the relationship (see the SI for full statistics). Regression coefficients greater than zero imply that likelihood of satisfaction increases with footprints. Negative coefficients indicate a negative correlation between satisfaction and footprints.

Subsistence

Protection

Identity

Creation

Freedom

Leisure

Understanding

Participation