We may need to add a new category: Totalitarian Delusions of Grandeur
You vil live in your pod, eat slurry, and like it! Nothing like modeling “six dimensions of human need satisfaction”.
This paper says all the quiet parts out loud.
Author links open overlay panel JefimVogela Julia K.Steinbergerba Daniel W.O’Neilla William F.Lambca JayaKrishnakumarda Sustainability Research Institute, School of Earth and Environment, University of Leeds, UKb Institute of Geography and Sustainability, Faculty of Geosciences and Environment, University of Lausanne, Switzerlandc Mercator Research Institute on Global Commons and Climate Change, Berlin, Germanyd Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva, Switzerland
Received 26 July 2020, Revised 27 April 2021, Accepted 7 May 2021, Available online 29 June 2021.
https://doi.org/10.1016/j.gloenvcha.2021.102287
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Highlights
- No country sufficiently meets human needs within sustainable levels of energy use.
- Need satisfaction and associated energy requirements depend on socio-economic setups.
- Public services are linked to higher need satisfaction and lower energy requirements.
- Economic growth is linked to lower need satisfaction and higher energy requirements.
- Countries with good socio-economic setups could likely meet needs at low energy use.
Abstract
Meeting human needs at sustainable levels of energy use is fundamental for avoiding catastrophic climate change and securing the well-being of all people. In the current political-economic regime, no country does so. Here, we assess which socio-economic conditions might enable societies to satisfy human needs at low energy use, to reconcile human well-being with climate mitigation.
Using a novel analytical framework alongside a novel multivariate regression-based moderation approach and data for 106 countries, we analyse how the relationship between energy use and six dimensions of human need satisfaction varies with a wide range of socio-economic factors relevant to the provisioning of goods and services (‘provisioning factors’). We find that factors such as public service quality, income equality, democracy, and electricity access are associated with higher need satisfaction and lower energy requirements (‘beneficial provisioning factors’). Conversely, extractivism and economic growth beyond moderate levels of affluence are associated with lower need satisfaction and greater energy requirements (‘detrimental provisioning factors’). Our results suggest that improving beneficial provisioning factors and abandoning detrimental ones could enable countries to provide sufficient need satisfaction at much lower, ecologically sustainable levels of energy use.
However, as key pillars of the required changes in provisioning run contrary to the dominant political-economic regime, a broader transformation of the economic system may be required to prioritise, and organise provisioning for, the satisfaction of human needs at low energy use.
Graphical abstract
Keywords
Sustainability Well-being Human needs Energy use Social provisioning Human development
1. Introduction
Limiting global warming to 1.5 °C without relying on negative emissions technologies requires not only rapid decarbonisation of global energy systems but also deep reductions in global energy use (Grubler et al., 2018, IPCC, 2018). At the same time, billions of people around the globe are still deprived of basic needs, and current routes to sufficient need satisfaction all seem to involve highly unsustainable levels of resource use (O’Neill et al., 2018). The way societies design their economies thus seems misaligned with the twin goals of meeting everyone’s needs and remaining within planetary boundaries (O’Neill et al., 2018, Raworth, 2017). This study addresses this issue by empirically assessing how the relationship between energy use and need satisfaction varies with the configurations of key socio-economic factors, and what configurations of these factors might enable societies to meet human needs within sustainable levels of energy use.
While these questions are poorly understood and empirically understudied (Brand Correa and Steinberger, 2017, Lamb and Steinberger, 2017, O’Neill et al., 2018, Roberts et al., 2020), the corner pieces of the research puzzle are largely in place. We roughly know the maximum level of final energy use (~27 GJ/cap) that can be globally rendered ecologically ‘sustainable’ (compatible with avoiding 1.5 °C of global warming without relying on negative emissions technologies) with deep transformations of energy systems (Grubler et al., 2018, IPCC, 2018). We understand what defines and characterises human needs, and what levels of which goods, services and conditions generally satisfy these needs (Doyal and Gough, 1991, Max-Neef, 1991, Millward-Hopkins et al., 2020, Rao and Min, 2018a).
We also know the basic characteristics of the cross-country relationship between energy use and a wide range of needs satisfaction indicators, including life expectancy, mortality, nourishment, education, and access to sanitation and drinking water (Burke, 2020, Lambert et al., 2014, Mazur and Rosa, 1974, Rao et al., 2014, Steinberger and Roberts, 2010). While at low levels of energy use, these need satisfaction indicators strongly improve with increasing energy use, they generally saturate at internationally moderate levels of energy use (ibid.). Beyond that saturation level, need satisfaction improvements with additional energy use quickly diminish, reflecting the satiability of needs (Doyal and Gough, 1991).
How much energy use is required to provide sufficient need satisfaction is only scarcely researched, and the few existing estimates are broadly scattered (Rao et al., 2019). Empirical cross-national estimates include 25–40 GJ/cap primary energy use for life expectancy and literacy (Steinberger and Roberts, 2010), or 22–58 GJ/cap final energy use for life expectancy and composite basic needs access (Lamb and Rao, 2015). Empirically-driven bottom-up model studies estimate the final energy footprints of sufficient need satisfaction in India, South Africa and Brazil to range between 12 and 25 GJ/cap (Rao et al., 2019), based on Rao and Min’s (2018a) definition of ‘Decent Living Standards’ that meet human needs. Global bottom-up modelling studies involving stronger assumptions of technological efficiency and equity, respectively, suggest that by 2050, Decent Living Standards could be internationally provided with 27 GJ/cap (Grubler et al., 2018) or even just 13–18 GJ/cap final energy use (Millward-Hopkins et al., 2020). Together, these studies demonstrate that meeting everyone’s needs at sustainable levels of energy use is theoretically feasible with known technology.
What remains poorly understood, however, is how the relationship between human need satisfaction and energy use (or biophysical resource use) varies with different socio-economic factors (Lamb and Steinberger, 2017, O’Neill et al., 2018, Steinberger et al., 2020). A small number of studies offer initial insights. The environmental efficiency of life satisfaction, presented as a measure of sustainability, follows an inverted-U-shape with Gross Domestic Product (GDP), increases with trust, and decreases with income inequality (Knight and Rosa, 2011). The carbon or environmental intensities of life expectancy, understood as measures of unsustainability, increase with income inequality (Jorgenson, 2015), urbanisation (McGee et al., 2017) and world society integration (Givens, 2017). They furthermore follow a U-shape with GDP internationally (Dietz et al., 2012), though increasing with GDP in all regions but Africa (Jorgenson, 2014, Jorgenson and Givens, 2015), and show asymmetric relationships with economic growth and recession in ‘developed’ vs. ‘less developed’ countries (Greiner and McGee, 2020). Their associations with uneven trade integration and exchange vary with levels of development (Givens, 2018). Democracy is not significantly correlated with the environmental efficiency of life satisfaction (Knight and Rosa, 2011) nor with the energy intensity of life expectancy (Mayer, 2017). All of these studies either combine need satisfaction outcomes from societal activity and biophysical means to societal activity into a ratio metric, or analyse residuals from their regression. Hence, they do not specify how these socio-economic factors interact with the highly non-linear relationship between need satisfaction and biophysical resource use, or with the ability of countries to reach targets simultaneously for need satisfaction and energy (or resource) use.
The socio-economic conditions for satisfying human needs at low energy use have been highlighted as crucial areas of research (Brand Correa and Steinberger, 2017, Lamb and Steinberger, 2017, O’Neill et al., 2018, Roberts et al., 2020), but remain virtually unstudied. While the theoretical understanding of this issue has seen important advances (Bohnenberger, 2020, Hickel, 2020, Stratford, 2020, Stratford and O’Neill, 2020, Gough, 2017, Kallis et al., 2020, Parrique, 2019), empirical studies are almost entirely absent. Lamb, 2016a, Lamb, 2016b qualitatively discusses socio-economic factors in enabling low-energy (or low-carbon) development, but only for a small number of countries. Furthermore, Lamb et al. (2014) explore the cross-country relationship between life expectancy and carbon emissions in light of socio-economic drivers of emissions, but do not quantitatively assess how life expectancy is related to carbon emissions nor to socio-economic emissions drivers. Quantitative empirical cross-country analyses of the issue thus remain entirely absent.
We address these research gaps by making three contributions. First, we develop a novel analytical approach for empirically assessing the role of socio-economic factors as intermediaries moderating the relationship between energy use (as a means) and need satisfaction (as an end), thus analytically separating means, ends and intermediaries (Fig. 1). For this purpose, we adapt and operationalise a novel analytical framework proposed by O’Neill et al. (2018) which centres on provisioning systems as intermediaries between biophysical resource use and human well-being (Fig. 1A). Second, we apply this approach and framework for the first time, using data for 19 indicators and 106 countries to empirically analyse how the relationships between energy use and six dimensions of human need satisfaction vary with a range of political, economic, geographic and infrastructural ‘provisioning factors’ (Fig. 1B). Third, we assess which socio-economic conditions (i.e. which configurations of provisioning factors) might enable countries to provide sufficient need satisfaction within sustainable levels of energy use. Specifically, we address the following research questions:1)
What levels of energy use are associated with sufficient need satisfaction in the current international provisioning regime?2)
How does the relationship between energy use and human need satisfaction vary with the configurations of different provisioning factors?3)
Which configurations of provisioning factors are associated with socio-ecologically beneficial performance (higher achievements in, and lower energy requirements of, human need satisfaction), and which ones are associated with socio-ecologically detrimental performance (lower achievements in, and greater energy requirements of, need satisfaction)?4)
To what extent could countries with beneficial configurations of key provisioning factors achieve sufficient need satisfaction within sustainable levels of energy use?
The remainder of this article is structured as follows. We introduce our analytical framework and outline our analytical approach in Section 2. We describe our variables and data in Section 3, and detail our methods in Section 4. We present the results of our analysis in Section 5, and discuss them in Section 6. We summarise and conclude our analysis in Section 7.
2. Analytical framework and approach
Building on the work of O’Neill et al. (2018), our analytical framework (Fig. 1A) conceptualises the provisioning of human needs satisfaction in an Ends–Means spectrum (Daly, 1973). Our framework considers energy use as a means, and need satisfaction as an end, with provisioning factors as intermediaries that moderate the relationship between means and ends. We thus operationalise O’Neill et al.’s (2018) framework by reducing the sphere of biophysical resource use to energy use (for analytical focus), and reducing the sphere of human well-being to human need satisfaction (for analytical coherence). Our operationalisation of human need satisfaction follows Doyal and Gough’s (1991) Theory of Human Need, reflecting a eudaimonic understanding of well-being as enabled by the satisfaction of human needs, which can be evaluated based on objective measures (Brand Correa and Steinberger, 2017, Lamb and Steinberger, 2017).
The main advancement of our framework consists in operationalising the concept of provisioning systems (Brand Correa and Steinberger, 2017, Fanning et al., 2020, Lamb and Steinberger, 2017, O’Neill et al., 2018) by introducing the concept of ‘provisioning factors’.
Provisioning factors comprise all factors that characterise any element realising, or any aspect influencing, the provisioning of goods and services. This includes economic, political, institutional, infrastructural, geographic, technical, cultural and historical characteristics of provisioning systems (or the provisioning process), spanning the spheres of extraction, production, distribution, consumption and disposal. In other words, provisioning factors encompass all factors that affect how energy and resources are used to meet human needs (and other ends). For example, it matters whether provisioning caters to consumers with equal or unequal purchasing power, whether it occurs in an urban or rural context, in a growing or shrinking economy, whether electricity is available, and what transport infrastructure is in place. Provisioning factors are intermediaries that moderate the relationship between energy use and need satisfaction. Whereas provisioning systems are broad conceptual constructs that are difficult to measure, provisioning factors are tangible and measureable, and as such operational: provisioning factors characterise provisioning systems (or the provisioning process).
While interactions between energy use, provisioning factors and social outcomes may in principle go in all directions (Fanning et al., 2020, O’Neill et al., 2018), our focus here is on the role of provisioning factors for countries’ socio-ecological performance, i.e. their achievements in, and energy requirements of, human need satisfaction (Fig. 1A). We use regression-based moderation analysis (Section 4.2) to assess how the relationship between energy use and need satisfaction varies with different provisioning factors, and subsequently model that relationship for different configurations of each provisioning factor (Fig. 1B). We further estimate how multiple provisioning factors jointly interact with the relationship between need satisfaction and energy use, using multivariate regression analysis (Section 4.3). While these are established statistical techniques, the way we apply them to our analytical framework and research questions is novel. Our approach allows us to coherently assess and compare the interactions of a broad range of provisioning factors, not just with need satisfaction or its ratio with energy use, but with the relationship between need satisfaction and energy use, across the international spectrum.
The variables assessed in our analytic framework (listed in Fig. 1A and detailed in Table 1, Table 2) capture key dimensions of human need, key categories of provisioning (state provision, political economy, physical infrastructure and geography) as well as total final energy use. Based on our understanding of human need theory (Doyal and Gough, 1991, Max-Neef, 1991) and provisioning systems (Brand Correa and Steinberger, 2017, Gough, 2019, O’Neill et al., 2018, Fanning et al., 2020), we analyse electricity access, democratic quality and income equality as provisioning factors (intermediaries) rather than as indicators of human need satisfaction (outcomes).
Table 1. Human need satisfaction variables used in the analysis.
| Variable name | Description and [units] | Sufficiency threshold | Indicator source |
|---|---|---|---|
| Healthy life expectancy | Average healthy life expectancy at birth [years] | 65 years | IHME GBD |
| Sufficient nourishment | Percentage of population meeting dietary energy requirements [%], calculated as the reverse of Prevalence of undernoursihment, rescaled onto a scale from 0 to 100% | 95% | WB WDI 2020 |
| Drinking water access | Percentage of population with access to improved water source [%] | 95% | WB WDI 2017 |
| Safe sanitation access | Percentage of population with access to improved sanitation facilities [%] | 95% | WB WDI 2017 |
| Basic education | Education index [score] | score of 75 | UNDP HDR |
| Minimum income | Absence of income shortfall below $3.20/day [%], calculated as the reverse of the Poverty gap at $3.20 a day (2011 PPP) | 95% | WB WDI 2020 |
Saturation transformations are applied to all need satisfaction variables (see Supplementary Materials Section C.4.2). Indicator sources are: the Global Burden of Disease Study (IHME GBD; Institute for Health Metrics and Evaluation, 2017), the World Development Indicators (WB WDI; World Bank, 2017, World Bank, 2020), and the Human Development Report 2013 (UNDP HDR; UNDP, 2013).
Table 2. Provisioning factor variables used in the analysis.
| Variable name | Description and [units] | Trans-formation applied | Indicator source |
|---|---|---|---|
| Electricity access | Percentage of population with access to electricity [%] | Saturation | WB WDI 2017 |
| Access to clean fuels | Percentage of population with access to non-solid fuels [%] | Saturation | WB WDI 2017 |
| Trade & transport infrastructure | Quality of trade and transport-related infrastructure [score], component of the Logistics performance index | Identity | WB WDI 2017 |
| Urban population | Percentage of population living in urban areas [%] | Identity | WB WDI 2017 |
| Public service quality | Quality of public services, civil service, and policy implementation [score], calculated as Government effectiveness, rescaled onto a scale from 1 to 6 | Identity | WB WGI |
| Public health coverage | Percentage of total health expenditure covered by government, non-governmental organisations, and social health insurance funds [%] | Identity | WB WDI 2017 |
| Democratic quality | Ability to participate in selecting government, freedom of expression and association, free media [score], calculated as Voice and accountability, rescaled onto a scale from 1 to 6 | Saturation | WB WGI |
| Income equality | Equality in household disposable income [score], calculated as the reverse of the Gini index | Saturation | SWIID |
| Economic growth | 3-year (2010–2012) average percentage annual growth rate of GDP per capita in constant 2011 $ PPP [%], calculated based on Gujarati, 1995, pp. 169–171 | Identity | WB WDI 2017 |
| Extractivism | Share of total value generation obtained from total natural resource rents [% of GDP] | Logarithmic | WB WDI 2017 |
| Foreign direct investments | Share of foreign direct investments (net inflow) in total value generation [% of GDP] | Logarithmic | WB WDI 2017 |
| Trade penetration | Share of total value generation that is traded [% of GDP], calculated as Importvalue+Exportvalue | Identity | WB WDI 2020 |
Indicator sources are: the World Development Indicators (WB WDI; World Bank, 2017, World Bank, 2020), the Worldwide Governance Indicators (WB WGI; World Bank, 2018, Kaufmann et al., 2011), and the Standardized World Income Inequality Database v6.2 (SWIID; Solt, 2020).
Read the full paper here.
And don’t miss the following section, emphasis mine:
6.4. Paradigmatic provisioning factors: Economic growth and (in)equality
Our findings challenge the influential claim that economic growth is beneficial to human well-being. In fact, our results suggest that at moderate or high levels of energy use, economic growth is associated with socio-ecologically detrimental performance (lower achievements in, and greater energy requirements of, need satisfaction). Given the close coupling between economic activity and energy use (Steinberger et al., 2020), these findings imply that economic growth beyond moderate levels of affluence is socio-ecologically detrimental. At low levels of energy use (currently corresponding to low levels of affluence), economic growth exhibits no significant association with need satisfaction. Joint analysis with other provisioning factors corroborates the adverse outcomes associated with economic growth (Supplementary materials Table B.2). These findings run contrary to the near-universal policy goal of fostering economic growth. Due to our novel approach of analysing economic growth as a provisioning factor, our results analytically integrate multiple critiques of growth: the social limits and detriments of growth (Hirsch, 1976, Kallis, 2019, Mishan and Mishan, 1967, O’Neill, 2015); the ecological unsustainability of growth (Dietz and O’Neill, 2013, Jackson, 2017, Kallis, 2018, Kallis, 2019); and the incompatibility of growth with limiting global warming to 1.5 °C (Antonakakis et al., 2017, D’Alessandro et al., 2020, Haberl et al., 2020, Hickel and Kallis, 2020). Abandoning the pursuit of economic growth beyond moderate levels of affluence thus appears ecologically necessary and socially desirable. Rendering a non-growing economy socially sustainable will require a fundamental political-economic transformation to remove structural and institutional growth dependencies (Hickel, 2020, Hinton, 2020, Kallis et al., 2020, Parrique, 2019, Stratford, 2020, Stratford and O’Neill, 2020).
Our findings also add new perspectives to the controversial debate on how income (in)equality relates to energy use and carbon emissions (Grunewald et al., 2017, Jorgenson et al., 2016, Oswald et al., 2021, Rao and Min, 2018b). By assessing income equality as a provisioning factor, our analysis integrates previous findings related to both biophysical resource use and social outcomes. The positive association we find between income equality and socio-ecological performance supports claims that improving income equality is compatible with rapid climate mitigation (D’Alessandro et al., 2020, Oswald et al., 2021, Rao and Min, 2018b), beneficial for social outcomes (Wilkinson and Pickett, 2010) and favourable (Jorgenson, 2015, Knight and Rosa, 2011, Oswald et al., 2021) or even required (Gough, 2017) for reconciling human well-being with ecological sustainability. These findings are particularly important as inequality is on the rise in many countries (Piketty and Saez, 2014), and as efforts to limit resource use could lead to escalating inequality through intensified economic rent extraction (Stratford, 2020). Taken together, these analyses provide a strong case for redistributive policies that establish both minimum and maximum income and/or consumption levels (Alexander, 2014, Fuchs and Di Giulio, 2016, Gough, 2020).
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