Environmentally Extended Input-output Research Articles

How does the establishment of an Indonesian Environmentally Extended Input Output (EEIO) model pave the way for Indonesia’s carbon future?

How does the establishment of an Indonesian Environmentally Extended Input Output (EEIO) model pave the way for Indonesia’s carbon future?

Estimating the carbon footprint of Mexican food consumption based on a high-resolution environmentally extended input-output model.

Increased global attention is being paid to the food-health-climate trilemma. In this study, we evaluate the climate impacts of Mexico's food consumption patterns by creating a high-resolution (262 sectors) Environmentally Extended Input-Output (EEIO) model called MXEEIO. We focus on the differences between food away from home (FAFH) and food at home (FAH) and compare Mexico's results with those of the USA. The results show that the main components of food spending in Mexico were meat, baked products, and beverages, raising concerns about their potential negative health effects if consumed excessively. Mexico's total greenhouse gas (GHG) emissions from food consumption were estimated at 149 million metric tons (MMT) in 2013, as opposed to 797 MMT for the USA. Meat and dairy products were the main contributors to Mexico's food-related GHG emissions, accounting for 57% of total emissions. Mexico spent a much smaller proportion of food-related income on FAFH than the USA (13% vs. 52%), suggesting great potential for growth as Mexico's per capita GDP continues to rise. Detailed contribution analysis shows that reducing Mexico's food-related GHG emissions would benefit most from a transition to low-carbon cattle farming, but mitigation efforts in other sectors such as crop cultivation and electricity generation are also important. Overall, our study underscores the significance of food-related GHG emissions in Mexico, especially those from meat and dairy products, and the mitigation challenges these sectors face.

Reducing global food loss and waste could improve air quality and lower the risk of premature mortality

While the global food system substantially contributes to environmental degradation and climate change, significant amounts of lost or wasted foods along the food supply chain actively contribute to global air pollution and related health risks. In this study, we use an environmentally-extended input–output model to quantify air pollution embedded in global food loss and waste (FLW) and investigate how FLW reduction policies can mitigate air pollution linked to food consumption, decreasing associated premature mortality risks across global regions. While estimating a positive impact of FLW reduction policies on decreasing air pollution levels (from −1.5% of SO2 emissions to −10.2% of NH3 emissions) and mortality reductions (over 67 000 lives worldwide) our findings highlight that rebound effects, wherein a reallocation of consumption from food to non-food commodities, decrease health and environmental benefits by over three quarters (compared to the case with no rebound). Such rebound effects can be substantially mitigated when final consumption shifts towards less pollution-intensive products, such as service activities, rather than conforming to the current composition of non-food consumption. Our results suggest that FLW-related policies would benefit from complementary measures that incentivize sustainable non-food consumption to effectively foster the transition towards a healthier and more sustainable planet.

Should the Carbon Footprint of Care be Taken into Account When Choosing a Treatment for Lymphoma? a Case Application on Mantle Cell Lymphoma

OBJECTIVE While climate change represents the greatest threat of the current century on human health, the carbon footprint of the healthcare sector represents 7-10% of occidental countries green house gas (GHG) emissions. Purchases, mostly of drugs and medical devices, represent about 50% of emissions. Oncology and hematology are suspected to be particularly carbon intensive, as they require long term and frequent consumption of healthcare resources. Here, we estimate the carbon footprint associated with different therapeutic strategies (including chemo-free or high dose chemotherapy) in patients with Mantle Cell Lymphoma (MCL) and discuss their future health impacts. METHODS Standard immunochemotherapy based versus chemo-free strategies were compared both in 1) the ≤65 years old/fit and 2) in the >65 years old/unfit populations. Namely, we compared 1) immunochemotherapy induction (rituximab, dexamethasone, cytarabine, and a platinum derivative) followed by autologous stem-cell transplantation and 3-year rituximab maintenance (LyMa) to obinituzumab, ibrutinib, venetoclax combination (OIV), and 2) rituximab, bendamustine combination (RB) to rituximab, ibrutinib combination (RI). These treatment schemes were considered to have similar clinical efficacy in each sub-group of patients. Carbon footprints were estimated by considering drugs, medical devices, hospital stays, car journeys, medical biology and imaging consumption in the French context. Drug specific method (including production, packaging and transport with or without R&D, sales, general and administrative costs), and non-drug specific methods using mean emission factors based on their costs (kgCO2eq/€ from the French Environment Agency (ADEME) and the US Environmentally-Extended Input-Output (USEEIO) models) were applied to estimate drug carbon footprints and were compared in a sensitivity analysis. Carbon footprints were translated into future disability adjusted life years (DALY, a metric similar to quality adjusted life years QALY) expected to be lost in the future using the ReCiPe 2016 model. RESULTS The carbon footprint of each therapeutic strategy varied from 6.6 to 69.1 tCO2eq. In each, >70% of the total carbon footprint was related to drug purchase. A larger fraction (10-70%) of the carbon footprint of biotherapies (rituximab, obinituzumab) and low-cost small molecules (cotrimoxazole, valaciclovir) is related to their production while it is smaller (<10%) for high-cost small molecules (ibrutinib, venetoclax) that require higher R&D, sales, general and administrative costs. In the ≤65 years old/fit population, treating a patient with LyMa leads to the emission of 16.8 tCO2eq corresponding to an estimated 0.21 [95%CI 0.08-0.51] induced DALY. Treating a patient with OIV for 5 years leads to the emission of 69.1 t CO2eq (0.86 [0.35-2.09] DALY) whereas stopping the treatment at 2 years leads to the emission of 36.6 tCO2eq (0.46 [0.19-1.11] DALY). In the >65 years old/unfit population, treating a patient with RB leads to the emission of 6.6 tCO2eq (0.08 [0.03-0.2] DALY) while treating a patient with RI for 5 years leads to the emission of 41.1 tCO2eq (0.51 [0.21-1.25] DALY) whereas stopping the treatment at 2 years leads to the emission of 22.7 tCO2eq (0.28 [0.12-0.69] DALY). These estimates have to be taken with caution, as they are highly dependent on the method chosen. Using non-drug specific ADEME or USEEIO mean emission factors increase the carbon footprint of 2 year OIV from 36.6 tCO2eq to 164 tCO2eq and 313 tCO2eq, respectively. On the contrary, only accounting for drug production, packaging and transport diminishes its carbon footprint to 10.8 tCO2eq. Detailed discussion on the models, on the impact of R&D and on the use of generic/biosimilar drugs will be provided at the congress. CONCLUSIONS Treatment of patients with MCL is associated with a significant carbon footprint. Integrating their carbon footprint in the therapeutic decisions (such as length of treatment) could lead to significant decreases in carbon emissions that could translate into less health damages to future generations. Drug specific carbon accounting methods seem more adapted than nonspecific ones to estimate the carbon footprint of high cost drugs . Future research is required to determine more precisely the carbon footprint of drugs and therapeutic strategies in patients with hematological malignancies.

Integrated assessment models and input–output analysis: bridging fields for advancing sustainability scenarios research

Technology-rich Integrated assessment models (IAMs) and Environmentally-Extended Input–Output Analysis (EEIOA) are widely employed for sustainability analysis, each offering unique strengths. IAMs focus on forward-looking scenarios, exploring technological shifts and climate change mitigation costs. EEIOA provides more comprehensive but static assessments of environmental and socio-economic impacts throughout supply chains, adopting a lifecycle perspective. I conduct a literature review to assess the current state of IAM-IO integration, paving the way for future research opportunities with advanced models. Existing studies have loosely linked IAM and IO models to improve one field or the other. This perspective highlights the potential for more advanced IAM-IO model linking and identifies three domains within sustainability scenarios research where IAM-IO integration could play a crucial role: the energy-industry nexus in decarbonization pathways, multi-dimensional sustainability impact assessment and demand-side solutions and post-growth climate mitigation scenarios. The expected research insights may be pivotal to design effective sustainable policies.

Environmentally-extended input-output analyses efficiently sketch large-scale environmental transition plans: Illustration by Canada's road industry

Industries struggle to build robust environmental transition plans as they lack the tools to quantify their ecological responsibility over their value chain. Companies mostly turn to sole greenhouse gas (GHG) emissions reporting or time-intensive Life Cycle Assessment (LCA), while Environmentally-Extended Input-Output (EEIO) analysis is more efficient on a wider scale. We illustrate EEIO analysis' usefulness to sketch transition plans on the example of Canada's road industry: estimation of national environmental contributions, most important environmental issues, main potential transition levers of the sector, and metrics prioritization for green purchase plans. To do so, openIO-Canada, a new Canadian EEIO database, coupled with IMPACT World+ v1.30–1.48 characterization method, provides a multicriteria environmental diagnosis of Canada's economy. The construction sector carries the second-highest environmental impacts of Canada (8–31% depending on the indicator) after the manufacturing industry (20–54%). The road industry generates a limited impact (0.5–1.8%), and emits 1.0% of Canadians' GHGs, mainly due to asphalt mix materials (28%), bridges and engineering structures materials (24%), and direct emissions (17%). The industry must reduce the environmental burden from material purchases - mainly concrete and asphalt products - through green purchase plans and eco-design and invest in new machinery powered with cleaner energies such as low-carbon electricity or bioenergies. EEIO analysis also captures impacts often neglected in process-based pavement LCAs - amortization of capital goods, staff consumptions, and services – and shows some substantial impacts advocating for enlarging system boundaries in standard LCA. Yet, pavement construction and maintenance only explain 5% of the life cycle carbon footprint of Canada's road network, against 95% for the roads' usage (72% from vehicle tailpipes releases, 23% for manufacturing vehicles). Thereby, a carbon-neutral pathway for the road industry must first focus on reducing vehicle consumption and wear through better design and maintenance of roads. Finally, EEIO databases and analysis must be developed further as a powerful tool to fight planet degradation, and openIO-Canada must be specifically expanded and refined to allow for more robust and larger multicriteria assessments.

Characteristics, temporal trends, and driving factors of household carbon inequality in India

The distribution of carbon space across households needs attention towards unraveling its temporal dynamics and drivers for designing just and equitable mitigation policies. This study focuses on the Indian economy from 2003–04 to 2015–16 as it aims to understand the characteristics, temporal trends, and driving factors of inequality in household carbon footprints for rural and urban areas. Utilizing Environmentally extended Input-Output (EE-IO) data along with national household survey-based micro-economic details of the Indian economy, we estimate the direct and indirect CO2 emissions associated with the consumption of regional expenditure deciles for both years. We first quantify the inequality in the distribution of these emissions across household deciles using the Gini coefficient, Theil index, and carbon Palma ratio to analyze their patterns and temporal trends. Secondly, we investigate the drivers of household carbon inequality and its variation over time using decomposition by regional population sub-groups, consumption category sources, and expenditures as a determinant.For 2015–16, the urban richest decile emits 13.3 times more than the poorest one, while this disparity is around 7.5 times only for the rural regions of the Indian economy. Our results indicate that inequality of carbon space sharing among Indian households increased over the 12 years, more conspicuously in the case of rural households than for urban counterparts. The rural carbon Gini increased from 0.308 to 0.324, while the urban one showed a negligible change from 0.398 to 0.400. The dominant role of household consumption basket changes over time, particularly towards transportation needs, is observed in contributing to increments in both regions' carbon inequality. On the other hand, the expenditure inequality shows a more prominent increase over time in the case of urban areas, with its expenditure Gini rising by 0.23 points compared to 0.19 points increment for the rural case. The reasons underlying the striking regional trends in carbon and expenditure Gini indices are explored. Implications of our results are relevant in informing climate justice and mitigation concerns.

Foreign Direct Investment and Air Pollution: Re-Estimating the “Pollution Haven Hypothesis” in China

This paper focuses on the relationship between foreign direct investment (FDI) and air pollution. Based on the relaxation of China’s FDI regulation policy as well as the “China Environmentally Extended Input-Output” database, we utilize a difference-in-differences methodology and investigate the casual effects of FDI liberalization on air pollution intensity. The empirical results demonstrate that FDI reduces the air pollution intensity. However, it is more pronounced in sectors with higher levels of absorption ability and human capital. The reduction in air pollution is mainly driven by technical effects through the enhancement of total factor productivity and technological efficiency. FDI can help achieve the green development goals in developing countries with a more liberalized policy.

Characterization of Greenhouse Gases Emissions by Economic Sectors Using Environmentally Extended Input-Output Analysis (EEIOA)

Objectives : As climate change deepened, the concept of 2050 carbon neutrality was introduced. The COP 26 Glasgow Agreement strengthened the national greenhouse gases (GHGs) target of 2030. However, there are controversies over the feasibility of these GHGs reduction goals, considering the economic sectors energy policy and the attributes of GHGs emissions. Taking this into consideration, this study aimed to formulate the 2017 Environmentally Expanded Input-output Table to analyze the characteristics of GHGs emissions in Korea's economic sectors.Methods : The carbon dioxide emission was calculated by multiplying carbon dioxide emission factors to fuel consumption in the 2017 energy balance table, while other GHGs emissions are taken from the national GHGs inventory. All the GHGs emissions calculated and taken were allocated to 381 basic sectors of the Input-Output Table to represent each sector's characteristics of GHGs emissions. Then 381 sectors are combined into a large category of 35 sectors to formulate the 2017 Republic of Korea Environmentally Extended Input-Output Table (ROKEEIOT). Using this ROKEEIOT, the emission of Scope 1. 2, and 3 by economic sectors were estimated, and the GHGs emissions and GHGs intensity by economic sectors.Results and Discussion : The carbon dioxide emissions calculated by the 2017 ROKEEIOT prepared in this study showed a difference of 2% from the 2017 national GHGs emission statistics, confirming that the 2017 ROKEEIOT is very effective. The three economic sectors with the highest direct GHGs emissions were electricity, steam, chilled or hot water, air conditioning supply (262,280 kt CO2eq.), primary metal products (117.098 kt CO2eq.), and transportation equipment (58,332 kt CO2eq.). However, the total GHGs emissions were different in the order of construction (151,476 kt CO2eq.), transportation equipment (112,168 kt CO2eq.), computer, and electronic and optical instruments (107,868 kt CO2eq.). As a result of classifying the scope of GHGs emissions, 6 industries exceeded 50% in Scope 1, and 29 sectors exceeded 50% in Scope 3, indicating that GHGs reduction measures were necessary for the supply chain in consideration of the GHGs emissions characteristics by economic sectors. In particular, 41.68% of GHGs emissions induced by final demand were generated by exports confirming, confirming the urgent need to strengthen the carbon competitiveness of export industry in preparation for the introduction of the carbon border adjustment mechanism.Conclusion : The economic contribution, direct GHGs emissions of industries, and the amount of GHGs emissions induced by supply chains and value chains show very different patterns by economic sectors. Therefore, it was confirmed that scientific policies to reduce GHGs emissions, such as net-zero and climate change measures, should reflect the characteristics of Scope 1, which is direct emission by industry, Scope 2 emissions caused by electricity and steam consumption and scope 3 emissions generated along the supply chain or value chain.

Optimization based structural decomposition analysis as a tool for supporting environmental policymaking

A new tool for supporting environmental policymaking presented in this paper combines the ease of interpretation of structural decomposition analysis in the environmentally extended input-output (EEIO) model with versality of MINLP optimization. The approach allows finding those coefficients in EEIO model in the case of which a small change of their levels leads to a large reduction of industrial GHG emissions. In the illustrative empirical example the proposed approach is first implemented in GAMS and next used to identify GHG-emissions-important IO coefficients in Poland.

Incorporating New Technologies in EEIO Models.

We propose a methodology to add new technologies into Environmentally Extended Input–Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model. In the Use table, a percentage of the current use of the existing commodity is transferred to the new commodity. The Supply or Make table is modified assuming that the new industries are the only ones producing the new commodity. We illustrate the method for the USEEIO model, for the addition of second generation biofuels, including naphtha, jet fuel and diesel fuel. The new industries’ inputs, outputs and value-added components needed to produce the new commodity are drawn from process-based life cycle inventories (LCIs). Process-based LCI inputs and outputs per physical functional unit are transformed to prices and assigned to commodities and environmental flow categories for the EEIO model. This methodology is designed to evaluate the environmental impacts of substituting products in the current US economy with bio-versions, produced by new technologies, that are intended to reduce negative environmental impacts. However, it can be applied for any new commodity for which the substitution assumption is reasonable.

FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries.

Quantifying industry consumption or production of resources, wastes, emissions, and losses-collectively called flows-is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges surrounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA's US Environmentally Extended Input-Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA's capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA's Data Commons.

Useeior: An Open-Source R Package for Building and Using US Environmentally-Extended Input–Output Models

useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model.

Pre-construction quantification of embodied environmental impacts to promote sustainable construction projects: The case study of a diversion dam

A quantitative assessment of the embodied environmental impacts of infrastructures can provide initial guidance to industry practitioners and engineers at the outset of the construction projects. This study presents the applicability of the Environmentally Extended Input-Output Analysis as a pre-construction evaluation tool for quantifying the embodied environmental impacts of a small diversion dam in Spain. Seven impact categories are assessed from a production-based and consumption-based perspective identifying the main sectors and regions contributing to the environmental impacts. From the consumption-based perspective, Spain is the only contributor to the environmental impacts, and from the production-based perspective, Spain is the main polluting country in all the impact categories contributing on average 68.9%. The use of high quantities of construction material leads to the sectors of steel and cement to significantly increase the environmental impacts. From the production-based perspective, steel and cement contribute on average 29.5% and 17.2%, respectively. From the consumption-based perspective, both sectors account for 74% of the overall environmental impacts. As observed in this case study, the application of the EEIOA model enables engineers and designers to focus during the early design stages on decisions that achieve high embodied impact reductions, such as prioritizing recycled materials for the construction of this infrastructure and, when possible, use materials from a less polluting origin.

An environmentally extended global multi-regional input-output analysis of consumption-based and embodied import-based carbon emissions of Turkey.

Understanding the consumption-based accounting (CBA), production-based accounting (PBA), and emissions embodied in trade is an important prerequisite for designing climate mitigation policies. Environmentally extended input–output (EEIO) models have been developed to evaluate the linkages between economic activities and environmental impacts as well as the embodied emissions in goods and services that are traded between countries. In this study, an environmentally extended global multi-regional input–output (EE GMRIO) analysis is performed to calculate Turkey’s CBA emissions and import-based embodied emissions for the year 2015 using the Eora26 database, which is a simplified version of the Eora database adapted to 26 economic sectors. The key sectors and sectoral carbon intensities of countries are determined in terms of embodied emissions in imports for household consumption. Our results indicate that Turkey was a net importer of greenhouse gas (GHG) emissions in 2015 and about 10% of total emissions of the final consumption in Turkey have occurred in other countries. The dominant contributing sectors to a nation’s GHG emissions can be quite different for the CBA and PBA approaches and the efforts to reduce GHG emissions requires a holistic approach. Import-based household emissions are assessed in terms of countries, sector and GHG intensities. Our results indicate that Turkey was a net importer of GHG emissions in 2015 with its approximately 10% of the total and 7.7% of household final consumption emissions having occurred in other countries. This also suggests that imported goods and services for household consumption have been produced in those countries with relatively low emission intensities. Considering Turkey’s emissions reduction targets, these results provide methodological benefits that will enhance national efforts by giving invaluable inputs about the emission intensity of imported and exported goods and better guidance to policy makers about future strategies for low-carbon manufacturing and shifting consumption patterns.

Capital, energy and carbon in the United States economy

Energy in economies is required both to build the stock of capital assets, and to produce goods and services from use of the stock. Decarbonization involves: changing the capital assets for producing energy; building and using capital with greater energy efficiency; and changing the distribution and characteristics of asset types in the capital stock. From 2004 to 2019, US capital assets grew by 24%, while primary energy consumption remained relatively unchanged. Here we examine the relationship between capital and energy in the US economy to understand how this growth occurred without increased energy use. The method involves mapping of energy use and greenhouse emissions onto four broad categories of capital stock, employing environmentally-extended input–output analysis and time-series data from a variety of sources. Result show that intellectual property products, with an energy intensity of 2.9 TJ / $million of investment, grew as a proportion of the capital stock relative to structures (7.0 TJ / $million) and equipment (6.9 TJ / $million). Meanwhile the energy intensity of using capital decreased in the residential sector and in all nineteen manufacturing sectors. The study connects strategies for decarbonization of the US economy in a concise and consistent framework, with turnover of the capital stock providing opportunities for both energy efficiency and decarbonization.

Examining embodied carbon emission flow relationships among different industrial sectors in China

Similar to the carbon transfer among regions and countries, the embodied carbon emissions among industries also have serious carbon leakage problems. It is important to determine the embodied carbon flow relationships among industrial sectors, to clarify the responsibilities of each sector, and to adjust the sectoral structure accordingly. Given that, an embodied carbon emission flow relationships network among industrial sectors (ECEFRN) is developed in this paper. In detail, an environmentally extended input-output (EEIO) model is applied to quantitatively estimate the embodied carbon emission flow relationships among industrial sectors in China. The roles and functions of each industrial sector in the network and the evolution characteristics of the embodied carbon emission flow relationships are determined by the social network analysis (SNA) method. Results show that ECEFRN has the small-world characteristics, and key industrial sectors in the network are identified based on the analysis of degree centrality and betweenness centrality. During the study period, in the ECEFRN, “Processing of petroleum, coking, and nuclear fuel”, “Smelting and pressing of metals”, “Production and supply of electricity and heat” are important “suppliers”. “Construction”, “Other service” and “Manufacture of transport equipment” are important “importers”. “Manufacture of foods and tobacco”, “Manufacture of transport equipment”, “Manufacture of computers, communication and other electronic equipment” are important “intermediaries”. Based on the aforementioned research results, this paper proposes corresponding policy suggestions. Network-based research has contributed more insights to the understanding of carbon emission reduction responsibilities among industrial sectors. These findings will provide certain theoretical support and policy references for policymakers when formulating policies for carbon emission reduction and industrial structure adjustment.

Sub-national water–food–labour nexus in Colombia

Poorer countries often face a severe trade-off: the need to improve socio-economic conditions is hard to balance with the maintenance of key ecological processes. As a case study, we select Colombia, a Latin American country with almost 10% of its inhabitants living in extreme poverty. We elaborate a water–food–labour (WFL) nexus grounded on a sub-national Environmentally Extended Input–Output (EEIO) analysis to assess the virtual water trade (VWT) and virtual informal labour (VIL) flows across administrative departments and economic sectors related to domestic trade. The main results are the following: high cross-departmental resource interdependence both in terms of VWT and VIL, rich departments highly depend on the resources of their neighbouring trading partners, extreme poverty conditions shown by economically isolated departments, and considerable income inequality in the food production sectors. Moreover, departments that are net exporters of virtual water suffer from water stress that might be exacerbated by future high rainfall variability due to climate change. These results suggest that strategies to attain sustainable development goals (SDGs) must deal with the biophysical constraints and the economic and political feasibility of the proposed solutions. In this vein, we argue that a holistic framework, grounded on quantitative analyses, is necessary to support informed policy decisions for the simultaneous achievement of multiple (possibly contrasting) goals. Moreover, severe spatial imbalances call for local policy responses coordinated at the national level.

Environmental Responsibility and Exposure of Finance: Combining Environmentally-Extended Input-Output and Balance Sheet Approaches

Environmental Responsibility and Exposure of Finance: Combining Environmentally-Extended Input-Output and Balance Sheet Approaches

Macro Sustainability across Countries: Key Sector Environmentally Extended Input-Output Analysis

When formulating economic development strategies, the environment and society must be considered to preserve well-being. This paper proposes a comparative sustainability assessment method using environmentally extended input-output analysis and multi-criteria decision aid. Using symmetric input-output tables and sectoral CO2 emissions and employment data for six countries, linkage coefficients are calculated for 163 sectors in each country. Multi-criteria decision aid tool, ELECTRE III, is used to derive outranking relationships among each country’s sectors using these coefficients as criteria, resulting in a hierarchy of sectors ordered by sustainability. Sectors that frequently appear at the top of the six hierarchies included education, health care, construction, and financial intermediation. China’s results differ significantly because of its concentration of economic activity on the primary/secondary sectors. The results can enable identification of key intervention pathways along which sustainable development could be stimulated. Country-specific recommendations and reflections on economic and sustainability policy initiatives are discussed.