Alongside climate change, eradicating poverty and hunger, reducing inequalities, and increasing access to basic needs, e.g. sanitation, energy, education, and other public services, are some of the biggest challenges of our actual society. However, despite decades of effort to provide a decent life for all, the increase of natural disasters due to climate change, the COVID-19 pandemic, and the war in Ukraine and African countries is reversing the positive trend achieved so far [1], [2].
However, there is no consensus on the basic access levels for a number of specific services, including public buildings and infrastructure, clothing, or nutrition that enable well-being. In other words, how can we provide a decent living for all while minimizing material depletion? A recent study by Vélez-Henao and Pauliuk titled “Material Requirements of Decent Living Standards” [3] delves deeply into this critical issue, offering a fresh perspective on the relationship between material (stocks and services) consumption and human well-being.
The Decent Living Standards Framework
Research on well-being can be framed in two broad schools of thought: hedonic, that is, happiness or subjective well-being, and eudaimonic, that is, objective well-being, which includes theories of human needs, capabilities, and multidimensional poverty [4]. The former is based on the idea that a good life is a matter of balancing pleasure over pain, enjoying life, and feeling good, while the latter is based on the idea that human well-being is derived from ‘flourish’ and lies distinct from a state of happiness or pleasure, that is, activities, abilities, or services (rather than goods) that constitute a well-lived life [4].
The decent living standards (DLS) is framed in the latter school of thought. The concept was developed by Rao and Min [5] to refer to a bundle of services e.g., nutrition, shelter, mobility, that are independent of sociocultural factors and subjective ideas, to be essential preconditions to human well-being. These services can be interpreted as the minimum requirements that enable a DLS. The DLS offers a simple but efficient approach to understand the tradeoffs between poverty and climate change, as well as a framework to estimate the demand for energy and materials required to achieve the SDGs (e.g., no poverty (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), quality education (SDG 4), and reduced inequalities (SDG 10))[3].
The DLS framework has been used for different colleagues to estimate the energy necessary to supply a well-being in various social-cultural contexts (between 13– 40 GJ per capita globally) [6], [7] and to calculate the energy required to fill the gap between current multidimensional poverty and the DLS 9-36 GJ/(capita*yr) [8]. These findings provide valuable information on DLS in low-energy demand scenarios in line with temperature targets of 1.5 – 2°C; however, they do not address the link between DLS material consumption [3].
Why are materials important to the DLS?
Materials such as steel, wood, and concrete form an important link between poverty-related and environmental SDGs. On the one hand, eradication of poverty requires access to basic services, which require functioning products and material stocks for their provision. However, material production is a major contributor to greenhouse gas (GHG) emissions (23% of global GHG in 2015) [9] and is responsible for more than 90% of biodiversity loss and water stress [10]. Additionally, the consumption of materials is increasing at unsustainable rates (23-fold throughout the 20th century) [9], [11]. A trend that will likely continue to grow as the transition to cleaner energy and more efficient products requires a massive amount of materials; for example, photovoltaic and wind power plants require between 6-40 times more copper than conventional fossil fuel technologies [12].
The Material Requirements for a DLS
Our results suggest that providing a DLS requires a material footprint (MF) of about 6 t/(cap*yr) of which the dimension of nutrition (38%) and mobility (26%) are the categories that contribute the most to the total (see Figure 1(A)), while collective services account for less than 1% of the total impacts. Transport by car (19%) and electricity (17%) are the most significant contributors to the total MF, while food products contribute with around 37% of the total footprint (see Figure 1(B)).
The environmental flows that contribute the most to the MF are biomass (20%), gravel (16%) and hard coal (11%), while 14% of the total footprint comes from metal ores such as gangue and iron. Similarly, phosphorus and shale (non-metallic minerals) contribute with 9% of the total MF (see Figure 2(C)). Alternatively, by type of materials, the non-metallic minerals (such as sand, gravel, limestone, and clay) and fossil fuels are responsible for around 34% and 28% of the total footprint, respectively, while metal ores contribute with 18% of the total impacts (see Figure 1(D)).
Figure 1. Four perspectives on the total material requirements (TMR) of the Decent Living Standards (DLS) by DLS dimension 2(A), by provisioning services 1(B), by the top ten environmental flows 1(C), and by DLS dimension and type of material 1(D). The TMR for Figure 1(A to C) is 6 t/(cap*yr) matching values from 1(D). The width of the 1(D) bars is proportional to the TMR.
Stocks associated with the DLS
The direct stocks required to provide the DLS is ~32 t/cap, with 98% of the direct stocks associated with the building. Moreover, between 78- 91% of the materials of the building stock are non-metallic materials, whereas wood materials account for between 4%-18%. For the collective services, 82% of the total stocks are made up of non-metallic minerals, while wood and paper products, basic metals, and chemicals and rubber contribute with 14%, 4% and <1% of the remaining total respectively.
Alternatively, the material composition of stocks of the household appliances e.g., washing machine, cook stove, refrigerator, laptop, phone, router, and lamps, are mainly basic metals and chemicals, rubber, and plastics. The former represents between 28-89% of the total materials. The latter represents between 9-61% of the total materials. Similarly, the material composition of the transport stocks is mainly basic metals (between 71-85%) and chemicals, rubber, and plastics (between 10-29%).
On the other hand, indirect stocks i.e., the infrastructure and machinery needed to provide certain services, for transport roads, rails and other kinds of infrastructure is needed, associated with DLS are ~11 t/cap. Dimensions of nutrition (42%) and mobility (31%) contribute the most to indirect stocks, while collective services barely represent 3% of the total indirect stocks. In these dimensions, the non-metallic mineral and basic metal products jointly account for 92-98% of the total materials. A similar trend can be observed for the remaining DLS dimensions.
Looking at the types of indirect stock, it can be seen that 61% arise from the construction sector, in particular civil engineering projects such as roads, railways, tunnels and bridges, utility projects (e.g., pipelines, irrigation systems, reservoirs, and power plants), and engineering projects (e.g., refineries, chemical plants, waterways, dams, and outdoor sports facilities). Furthermore, the remaining 37% and 2% of indirect stocks come from the construction of buildings (residential and non-residential buildings such as factories, hospitals, hotels, airports, parking garages, warehouses, and religious buildings) and other sectors such as the manufacture of machinery and equipment and others, respectively.
The role of sufficiency and efficiency strategies on DLS provisioning.
Lifestyles and efficiency measures significantly influence the MF. The findings suggest that the DLS can be provided with an MF in the range of 3-14 t/(cap*yr) (see figure 2A). Taking nutrition as an example, reducing meat consumption by half or switching to a vegetarian or vegan diet reduces the MF of nutrition by 9, 24, and 35%, respectively. Alternatively, switching the consumption of staples from potatoes to wheat or rice will increase the MF between 22-34% respectively, mainly due to two factors. First, the yields for potatoes are between 4-5 times higher than for rice or wheat, respectively [13] and second, the amount of fertiliser (nitrogen N, phosphorus P, and potassium K) used per kg of potatoes (~0.04 kg fertilizers/kg) is less than the required for rice (~0.06 kg fertilizers/kg) or wheat (~0.08 fertilizers/kg) [13], [14]. Furthermore, switching to more efficient cooking appliances will reduce MF by 2%, while cooking with electricity instead of gas will increase MF by 10%.
Similarly, the MF of mobility is highly dependent on the transport modes (e.g., car, bus or train) and their powertrain (combustion or electric engine) used to meet the service demand. Compared to the reference scenario, a transition to electric vehicles would increase the mobility MF by 26%. This is mainly because electric vehicles require between one and four times more metals and minerals than ICEVs [15]. Active mobility measures such as walking and cycling and reducing the use of private vehicles reduce the MF. Covering short distances by walking instead of cycling leads to 3% savings. Alternatively, if at least 23% of the demand for private vehicles is shifted to public transport (train and bus), 10% savings can be anticipated.
The Implication of Renewable Energies and building archetypes on the DLS
Electricity is needed to provide a wide range of services, e.g., for cooking and conserving our food products, to illuminate our living rooms, power many household appliances and for mobility, for example, by using the train or electric vehicles. Therefore, it is not surprising that electricity accounts for around 17% of the total material impacts. Thus, using electricity mixes with more renewable energies contributes significantly to provide a DLS with clean energy and less materials. Our findings suggest that using the Norway mix that is mainly based on hydro (93% of the total shares) reduces the MF for providing a DLS to 5.2 t / (cap * year) or 14%. Alternatively, having a grid with high shares of renewable sources without nuclear sources (Uruguay) translates into 13% of the potential savings in providing a DLS. Alternatively, high shares of nuclear power (71%) (France) translates into 6% of the savings. While a grid with high shares of wind (36%) (Denmark) may reduce the MF by 5%.
However, a mix of high shares of fossil fuels (43%) and solar resources (13%) (German) will increase the MF by 1%. This is mainly because although the German mix has more solar (13%) and wind (18%) sources than the global mix (reference scenario 2% and 4%, respectively), the former has less hydro (4%) than the global mix (17%). In general, electricity mixes with high shares of renewables (especially hydro) help to provide a DLS with lower MF. Contrary to what one might expect due to the fossil fuel–metal ore linkage. See figure 2.
Figure 2. Scenario results for the total material requirements (TMR) footprint for the Decent Living Standard (DLS), showing lower and upper bond scenario results 2(A), TMR by selected electricity mix in kg/kWh 2(B) and TMR for DLS based on the reference scenario with different electricity mixes 2(C). The dashed lines in 2(A) represent the material cap target of IRP [16] and the current TMR for the selected countries from Bringezu [17]. Dashed lines Data in 2(C) represent the TMR of the reference scenario (6 t/(cap*yr)).
As mention above, buildings make up a large proportion of direct stocks (98% of the total), since their construction is highly material intensive and has a longer lifespan (≥80 yrs) than other stocks, such as cars (~ 15 yrs). Therefore, alternative construction processes, such as changing materials or making the construction lighter, are necessary to provide a low material intensity [18]. For example, increasing the amount of wood (from ~52 kg/m2 to ~120 kg/m2 or 131%) in a single-family household would reduce the MF 26%. Similarly, a lighter building that uses less concrete (from ~1.2 ton/ m2 to ~886 kg/m2 or 28%) and steel (from ~71 kg/ m2 to ~55 kg/m2 or 22%) would reduce MF 17%. Combining the two alternatives (more wood and less concrete and steel) will reduce MF by 28%. Alternatively, switching to a multi-family household reduces the MF 25%. While changing to a residential tower will increase the MF 67%. Finally, moving to a lighter, wood-based multi-family building would reduce the MF 43%.
Political Implications of the DLS and outlook
Currently, around 1.2 billion people live in multidimensional poverty (deprivations in health, education, and standard of living) [1]. Providing a DLS for these people will require a massive political effort mainly because it is not only a matter of income or even the services provided, but it is also about the required in-use stocks (direct and indirect) and the connected supply chains and environmental impacts associated. By simply scaling our results, we find that providing these people with DLS requires an MF of about 7.2 Pg/yr and around 51.6 Pg stocks (direct and indirect).
In the second step, the DLS requirements need to be detailed for specific regions, e.g., global South and North, or even more granular, to accurately consider the stage of development, the level of urbanization, transport infrastructure, and socio-cultural factors such as population size, age distribution, housing, diets, and social values that determine what a region perceives as a DLS.
The Paris agreement acknowledges the need to set climate goals that safeguard development rights [19]. However, there is a lack of articulation of what human development means under climate constraints in political debates. The DLS may serve as a baseline for defining such rights because they link human development with energy and material use and the associated climate impact and mitigation potential [6].
Finally, future efforts should focus on disaggregating the results into regions. With our results, the extensive SI and LCA approach own compositions of ‘DLS baskets’ by region/country are possible. Moreover, efforts should be made to estimate the amount of materials and stocks needed to close the gap between DLS and the actual poverty as Kikstra et al. [8].
The limitations and current uncertainties of the DLS material footprint are documented and discussed in [3]
References
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