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 CO 2 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.


Introduction
There is no one-size-fits-all approach to sustainable economic development that can be applied to every country, region, and city in the world. In the face of anthropogenic climate change, depletion of natural resources, dependence on fossil fuels, loss of biodiversity, and worsening socio-economic inequality; every region that intends to address these issues must tailor its sustainable development strategy in a way that considers its own unique set of economic, social, and environmental circumstances. Environmental protection and social well-being are global, not localized, issues that can only be effectively addressed in an international, coordinated manner. Global initiatives have been designed and undertaken to align different nations' sustainable development efforts towards agreed-upon goals, most notably and recently the 2015 Paris Agreement, which has been signed by 195 countries. The primary aim of the Paris Agreement is to mitigate anthropogenic climate change and ultimately hold global temperatures below 1.5 degrees Celsius above pre-industrial levels; a goal which will require careful consideration of direct and indirect GHG emissions resulting from industrial economic activity [1] (Savaresi, 2016). This paper aims to demonstrate a quantitative framework that can help individual countries most effectively identify how to sustainably manage their economies through key sector analysis, and explain the nature and causes of differences between key sectors across different countries. The six countries analyzed in this study are Austria, China, France, Germany, Sweden, and the USA. These countries were selected due to characteristics pertaining to their energy generation mix, degree of economic development, and social structure, among other factors that are further detailed in Section 2.4.
Understanding the complex internal structure of an economy allows one to gain a clearer picture of the nature and implications of its natural resource throughput as well as its impact on the wider social structure. Input-output analysis (I-O) is a simple yet powerful way of understanding the interdependencies of different components of an economy. EE I-O allows one to quantify the direct and indirect effects of economic activity on environmental and social indicators (CO 2 emissions and employment, respectively, in this study). The EE I-O method of key sector analysis is fundamental to gaining a clearer understanding of an economic structure and is employed to analyze the economies of the six countries. Key sectors can be calculated by different weightings, which in this case are different sectors' propensity for stimulating economy-wide final demand, CO 2 emissions, and employment as measured by forward and backward linkage coefficients. Using the MCDA method ELECTRE III, the forward and backward linkages are then used as criteria to form an outranking hierarchy, at the top of which is a proxy for the most sustainable sectors in each economy. The implications of these findings for the six countries is explored in a policy discussion, and this methodology could be extended to any number of countries in order to most optimally design their approach to sustainable development.

Methodological Literature Review
In this section the theoretical underpinnings, history, and recent applications of I-O, EE I-O and MCDA are explored. The justification for selecting each of the six countries is also described. There are large bodies of existing literature for EE I-O and MCDA, and this study aims to make a valuable contribution to that body of work by performing a comparative macro sustainability analysis using the novel framework which combines both key sector identification using EE I-O and MCDA in this manner.

Input-Output Analysis
The flow of resources into, within, and out of a region's economy involves several fundamental components including domestic economic sectors that produce and consume output (intermediate producers/consumers); households and the government that consume output (final consumers); and foreign nations that import and export goods and services into/out of the country. I-O is concerned with the intermediate sectors in an economy and the portions of their output that are then consumed by other intermediate sectors as inputs. The core element of an I-O model is a matrix showing resource flows (usually monetary) between economic sectors over a certain period of time (usually one year) [2] (Leontief, 1986). I-O, as a field of study, was developed by Russian-American economist Wassily Leontief, for which he won the 1973 Nobel Prize in Economics. Leontief was the first to represent intersectoral transactions as a matrix. Although his work resulted in the proliferation of the study and applications of I-O, important work by several others preceded him including Francois Quesnay's Tableau économique in 1758 [3] (He, 1972), and Léon Walras's Elements of Pure Economics in 1874 [4] (Walras, 2010). Balanced, symmetric I-O tables are derived from non-symmetric supply and use tables using methods including those outlined by [5] Eurostat (2008). The granularity of sector classification is arbitrary, and I-O tables are generated and used at a variety of sectoral resolutions (for example, the Electricity sector can be included as a whole, or it can be split into electricity generated from different sources such as coal, natural gas, nuclear, and renewables).
A variety of useful insights can be derived from I-O tables that reveal the structure of an economy and the interdependent relationships between its economic sectors. Leontief conceived the Leontief Inverse Matrix L, which is calculated as (I − A)-1 where I is the identity matrix and A is the coefficients matrix (each value z in the I-O matrix as a proportion of its total output) [2] (Leontief, 1986). The Leontief Inverse has served as the basis for extensive applied economic analysis since it was first developed. Leontief's breakthrough and seminal work on the subject was his 1941 book "The Structure of the American Economy, 1919-1939: An Empirical Application of Equilibrium Analysis" [6] (Leontief, 1941). In this work he validated, using 10-sector balanced input-output tables that were Sustainability 2021, 13, 11657 3 of 46 assembled using empirical data, that the general coefficient structure of the I-O tables was relatively stable over time, providing encouraging evidence that he had discovered a robust, accurately descriptive economic framework. In 1944, Leontief published "Output, Employment, Consumption, and Investment" [7] (Leontief, 1944), which applied I-O to the economic consequences of WWII. I-O was found to be practically useful to the degree that the United States Bureau of Labor Statistics adopted I-O as its preferred method of employment analysis following the war, and the government constructed a high-resolution I-O table that included 400 sectors [8] (Dorfman, 1973). Numerous other practical applications of I-O have emerged in the decades following Leontief's pioneering work and it has become a key method used in a variety of economic contexts around the world including tourism impact [9] (Fletcher, 1989), interregional trade analysis [10] (Moses, 1955), and evaluating the structure of income distribution [11] (Miyazawa, 2012), among many others.
A key concept in I-O that was developed as an extension to Leontief's work is forward and backward inter-industry linkages, which was a concept first introduced by Rasmussen [12] (1956). Forward and backward linkages are coefficients derived from the Leontief Inverse Matrix that measure the 'downstream' and 'upstream' direct and indirect influences of the activity of a particular sector on other sectors in an economy. To illustrate this in a simplified way, suppose an economy consists of three sectors; Agriculture, Manufacturing, and Transportation. Inputs into Agriculture include services provided by Transportation, which itself requires inputs from the Manufacturing sector, and so on. Therefore, expansion of the Agricultural sector results in indirect upstream expansion of both the Transportation and Manufacturing sectors. The degree to which this is the case is the backward linkage coefficient, and the same principle applies to forward linkages with downstream, consuming sectors. This methodology can be used to identify key sectors, which was an idea first proposed by [13] Hirschman (1958). Key sectors are those which have forward and backward linkage coefficients that are both greater than one, i.e., the sector is more deeply linked on average, in both directions, than all of the other sectors in the economy. Hirschman postulated that economic growth occurs primarily due to these key sectors and their above-average influence on the rest of the economy.
This  Miyazawa, 1971) which focused on multi-regional economic systems and income multipliers. Michael Sonis and his colleagues have developed and expanded upon key economic sectors and linkages further, specifically focusing on hierarchies of key linkages/pathways and multiplier product matrices ( [21] Sonis and Hewings, 1989

Environmentally Extended Input-Output Analysis
Environmentally extended input-output analysis (EE I-O) builds upon I-O and is an analytical technique that aims, broadly, to calculate the hidden, indirect, or embodied environmental and/or social impacts associated with an upstream economic event ( [37] Kitzes, 2013). EE I-O incorporates sectoral-level data on resource flows and usage (such as CO 2 emissions and employment) in order to calculate direct and indirect intensity of these flows in response to supply/demand stimuli. It has been conceptualized as a tool to present the complexity of economy-environment interactions by Daly [38] (1968), Ayres & Kneese [39] (1969) and Victor [40] (1971). Leontief was the first to extend input-output models to account for environmental impact and other resource use/emissions types in his 1970 paper "Environmental Repercussions and the Economic Structure: An Input-Output Approach" ([44] Leontief, 1970). In this paper he calculated technical input-output coefficients in order to analyze how growth in hypothetical economic sectors affected levels of corresponding pollution. Leontief Thomas, 1997). It has also informed the development of material flow accounting (MFA), which is the study of material flows on a national or regional scale ( [84]  More recent environmentally extended input-output contributions include studies by [96] Lenzen et al. (2012) who found that approximately 30% of global species threats (as represented by the IUCN red list) are due to international trade using EORA database. In their research that linked 25,000 species to more than 15,000 commodities produced in 187 countries and evaluated over 5 bln supply chains from the point of view of their biodiversity impacts, [96] Lenzen et al. (2012) found that USA, Japan, Germany, France, UK, Italy, Spain, South Korea, Canada are the top 'net importers' of biodiversity and Indonesia, Madagascar, Papua New Guinea, Malaysia, Philippines, Sri Lanka, Thailand, Russia, Cambodia, Cameroon are the top 'net exporters'. Such analysis was possible through the links between the threatened species and implicated commodities traded in international markets. The issues of the macroeconomic impacts of individual lifestyles have been addressed in [77,[97][98][99].

Multi-Criteria Decision Analysis
Once a sector's forward and backward linkage coefficients have been calculated and weighted by final demand, CO 2 emissions, and employment, MCDA can be used to bring that information together into one singular assessment of the most optimal trade-offs between those criteria. MCDA is a broad categorization of decision-making frameworks that allow alternatives to be compared to each other according to differing criteria which are often conflicting and incommensurable. MCDA is highly suitable to this application, where sectors serve as alternatives and their forward and backward linkages serve as criteria with which the alternatives are evaluated. MCDA was initially developed in the 1960s by Bernard Roy. Since then, several branches of methods have emerged over the subsequent decades ([100] Roy and Vanderpooten, 1996 Kananen et al. (1990), who evaluated emergency management techniques in response to the propagation of economic and political shocks through the input-output network of the Finnish economy; [74] Luptáčik and Böhm (1994), who employed a multi-objective model which aimed to minimize factor costs to produce Gross National Product (GNP) and also minimize net pollution; [42] Shmelev (2012), who used environmentally-weighted forward and backward linkage coefficients in combination with NAIADE to determine which sectors in the UK economy were the most sustainable; [124] Shmelev and Rodríguez-Labajos (2009), who assessed intertemporal macro sustainability in Austria over a 25-year period; and [125] Shmelev (2011), who used MCDA to assess macro sustainability progress over time in Russia according the UN Sustainability Development Framework of Indicators.
For this study, ELECTRE III was selected. ELECTRE III is an outranking method that was developed by Bernard Roy [101] (Roy, 1978), which incorporates realistic decisionmaking parameters for different criteria scores, namely an indifference threshold (below which one is indifferent to two alternatives by a given criterion), preference threshold (above which one displays a clear preference for one alternative over another by a given criterion), and a veto threshold (above which, for a certain criterion, an alternative outranks another by all criteria) [126] (Figueira et al., 2005). ELECTRE III was selected for this study because it is an outranking method which clearly displays the preference structure of alternatives, and because its incorporation of the aforementioned thresholds lends to a more realistic modelling of a decision-making process beyond simpler methods such as the weighted sum. It is a robust method that uses a strong sustainability concept (due to its non-compensatory nature), as opposed to the weak sustainability concept used by more rudimentary MCDA methods such as AHP (

Country Selection
Austria, China, France, Germany, Sweden, and the USA were selected for this study because of particular economic, environmental, and social traits of interest that they possess. A comparison of the six countries by two measures each of economic, environmental, and Sustainability 2021, 13, 11657 6 of 46 social development indicators (as defined by the UK Office for National Statistics) ( [130] Office for National Statistics, 2015) is shown below in Figure 1. These traits affect the structure of their economies in unique ways, and observing how these traits are revealed through descriptive EE I-O analysis could result in useful insights that policymakers can use to adapt sustainable development strategies to each of the countries. The justification for each country's inclusion in the study is set out below.
Austria was included because of the wide array of accurate data available ([124] Shmelev and Rodrigues-Labajos, 2009); the strong influence of the economy of neighboring Germany; and the importance of its primary sector compared to other similar, highlydeveloped countries.
China was selected due mainly to its relative concentration of economic activity in the primary and secondary sectors compared to the other countries analyzed. China has grown rapidly over the last (GDP growth was 3003.5% between 1990 to 2016, compared to the global average of 234.6%) ( [131] World Bank, 2017); it is a highly CO 2 emissionsintensive economy; and it acts as a point of contrast to the other five countries which are comparatively similar to each other.
France was selected primarily to explore the effect of the country's high degree of reliance on emissions-free nuclear energy, which made up 76.9% of the country's electricity generation in 2007 ( [132] World Nuclear Association, 2017), the year of the EXIOPOL I-O data used in this study.
Germany is a highly CO 2 emissions-intensive economy with a strong focus on its manufacturing sectors relative to other highly developed western nations; particularly its automotive manufacturing sector. Oil and coal supply 58.4% of the country's energy demand, compared to the EU average of 51.  The USA is included because of its size and global significance. The country made up 24.6% of global GDP in 2016 ( [131] World Bank, 2017). The USA differs in nature to the other five countries in several key ways, namely that it has much higher GDP per capita and CO2 emissions per capita ( Figure 1) and it has a much larger focus on public administration and defense. Its Gini coefficient is also high relative to the other highlydeveloped nations, implying that its vast wealth is particularly skewed towards the top of its socio-economic hierarchy.
Exploring differences in key sectors between these six countries will help to shed new light on the nature of their differences, provide insight into how sustainable development strategy should be adapted to meet the unique conditions of each nation, and also strengthen the robustness of environmentally extended input-output analysis as a tool for gaining accurate, data-driven insights into complex economies.

Selecting a Data Source
To effectively compare and contrast the six countries, a set of standardized I-O tables with corresponding sectoral final demand, CO2 emissions, and employment vectors were required. Many national governments compile and publicly release their own supply, use, and input-output tables along with labour statistics, but rarely do two countries' sector classifications align without some degree of modification or disaggregation. Fortunately, there are a number of sources of aggregated and standardized national input-output tables with corresponding environmental and resource accounts for multiple countries, five of which are compared in Table 1. The USA is included because of its size and global significance. The country made up 24.6% of global GDP in 2016 ( [131] World Bank, 2017). The USA differs in nature to the other five countries in several key ways, namely that it has much higher GDP per capita and CO 2 emissions per capita ( Figure 1) and it has a much larger focus on public administration and defense. Its Gini coefficient is also high relative to the other highlydeveloped nations, implying that its vast wealth is particularly skewed towards the top of its socio-economic hierarchy.
Exploring differences in key sectors between these six countries will help to shed new light on the nature of their differences, provide insight into how sustainable development strategy should be adapted to meet the unique conditions of each nation, and also strengthen the robustness of environmentally extended input-output analysis as a tool for gaining accurate, data-driven insights into complex economies.

Selecting a Data Source
To effectively compare and contrast the six countries, a set of standardized I-O tables with corresponding sectoral final demand, CO 2 emissions, and employment vectors were required. Many national governments compile and publicly release their own supply, use, and input-output tables along with labour statistics, but rarely do two countries' sector classifications align without some degree of modification or disaggregation. Fortunately, there are a number of sources of aggregated and standardized national input-output tables with corresponding environmental and resource accounts for multiple countries, five of which are compared in Table 1. EXIOPOL was selected as the data source for this study because: 1. It contains standardized, symmetric input-output tables for Austria, China, France, Germany, Sweden, and the USA; 2. It contains complete data for final demand as well as sector-disaggregated CO 2 emissions and employment accounts; 3. It has a very high sectoral resolution (163 sectors) including separate categories for different types of electricity generation and waste management.
A disadvantage to using EXIOPOL is that the most recent data available is from 2007, whereas other sources have more up-to-date data. This fact is outweighed by the benefits listed above and does not affect the ability to illustrate this study's methodology. EXIOPOL's wide array of environmental and resource accounts are not based entirely on empirical data because of information availability limitations. The process for calculating and disaggregating the environmental accounts used in EXIOPOL is described in [13] Tukker et al. (2013). For the purposes of this study the data is sufficient to illustrate the usefulness of the method and to make reasonably accurate calculations of the CO 2 emissions and employment forward and backward linkage coefficients.

Calculating Forward and Backward Linkage Coefficients
To calculate the forward and backward linkage coefficients for each industry weighted by each resource vector, the methodology used in Manfred Lenzen's 2003 paper 'Environmentally important paths, linkages and key sectors in the Australian economy' was employed ( [41] Lenzen, 2003). The forward linkage is the resource-weighted row average of the Leontief inverse L, which is then divided by the resource-weighted global average of L. The forward linkage is the resource-weighted column average of the Leontief inverse L, which is then divided by the resource-weighted global average of L.
A linkage coefficient represents how deeply linked a sector is relative to all of the linkages in the economy. For example, a sector with a forward CO 2 linkage coefficient of 1.5 is 50.0% more linked than the average of all sectors in the economy. A key sector is one which has both a forward and backward linkage greater than one, implying that is has an above-average ripple effect on economy-wide final demand, CO 2 emissions, or employment in response to stimulus/contraction in that sector.

Calculating Sector Sustainability with Multi-Criteria Decision Analysis
After the linkage coefficients were calculated for final demand, CO 2 emissions, and employment, an MCDA using ELECTRE III was used to determine which sectors have the most optimal balance of forward and backward linkage coefficients for the three categories. The intended output of this calculation is a proxy for the most sustainable economic sectors in the country in question. There are established methods in the literature that guide selection of realistic values for the indifference, preference, and threshold levels, which is inherently a highly subjective process. One such guideline is [142] Rogers and Bruen (1998a), which suggests that (a) the veto threshold v should be set closer to the preference threshold as adverse human reaction to the criteria increases and (b) that the preference threshold p and the indifference threshold i should be defined within relatively strict limits.
The threshold settings for this study are shown in Table 2. The thresholds were determined as a proportion of the difference between the highest and lowest linkage coefficients for each criteria. Indifference thresholds were set at 1.0% of the difference, preference thresholds were set at 1.1% (reflecting the first of Rogers and Bruen's [142] principles) and veto thresholds were set at 80.0% of the difference for final demand and employment, and 70.0% of the difference for CO 2 emissions (conforming to Rogers and Bruen's [142] second principle). Setting the indifference and preference thresholds so close together follows the concept of strong sustainability because there is a very small degree of compensation among criteria. All six criteria were equally-weighted at 16.67% each. Because of the volume of data produced for the six countries, different weighting schemes were not evaluated in this study due to scope constraints. Utilizing unequal weighting schemes is a direction for future research in this context.   Final demand key sectors in the USA are largely centered on the services/tertiary industries, with 18 tertiary sectors out of a total of 25 key sectors. Public administration and defence is deeply linked within the USA economy because of the government's high degree of military expenditure, which totaled 3.3% of its GDP in 2016 compared to the global average of 2.2% of GDP ( [131] World Bank, 2017). Real estate activities, Health and social work, and Construction rounded out the other most deeply linked sectors. Deeply linked secondary sectors in the USA include Construction, which is typical of most countries in this study and Manufacture of motor vehicles, trailers and semi-trailers, due to the USA' status as the second-largest motor vehicle producer in the world after China.

Forward and Backward Linkage Coefficients
In contrast to the USA, Germany's economy has more key sectors in the secondary/ manufacturing field (  In Austria, coal and natural gas supply 36.0% and 22.4% of total energy consumption, respectively ([133] BP, 2016). This is reflected in Figure 4 that as they are both deeply linked sectors as weighted by CO2 emissions. Manufacturing industries also have strong In Austria, coal and natural gas supply 36.0% and 22.4% of total energy consumption, respectively ([133] BP, 2016). This is reflected in Figure 4 that as they are both deeply linked sectors as weighted by CO 2 emissions. Manufacturing industries also have strong linkages throughout the Austrian economy, such as Manufacture of basic iron and steel and of ferroalloys and first products thereof, Manufacture of gas; distribution of gaseous fuels through mains, and Manufacture of cement, lime and plaster. Of note are sectors with very high backward linkages and forward linkages of zero, which are renewable energy generation sectors (Production of electricity by Geothermal, Production of electricity by solar photovoltaic, Production of electricity by wind, and Production of electricity not elsewhere classified). This phenomenon is unique to Austria among the six countries studied, and is important to note as the true indirect impact of renewables on CO 2 emissions in Austria may be significantly higher than previously thought.  By comparison, the key CO2 emissions sectors in France are shown in Figure 5. This structure is quite different than that of Austria. Because France generated 76.9% of its electricity from nuclear energy in 2007 ( [132] World Nuclear Association, 2017), electricity generation sectors tend to be less deeply linked in its economy weighted by CO2 emissions. This contrast is even more striking when compared to coal-focused economies such as Germany, the USA, and China.
The effect of Sea and coastal water transport is apparent compared to landlocked countries such as Austria. Other business activities have an unusually high forward CO2 emissions-weighted linkage, due mainly to its strong downstream linkages CO2 emissions-intense sectors such as Sea and coastal water transport; Inland water transport; and Man- By comparison, the key CO 2 emissions sectors in France are shown in Figure 5. This structure is quite different than that of Austria. Because France generated 76.9% of its electricity from nuclear energy in 2007 ( [132] World Nuclear Association, 2017), electricity generation sectors tend to be less deeply linked in its economy weighted by CO 2 emissions. This contrast is even more striking when compared to coal-focused economies such as Germany, the USA, and China.
The effect of Sea and coastal water transport is apparent compared to landlocked countries such as Austria. Other business activities have an unusually high forward CO 2 emissions-weighted linkage, due mainly to its strong downstream linkages CO 2 emissionsintense sectors such as Sea and coastal water transport; Inland water transport; and Manufacture of cement, lime and plaster. As can be seen in Figure 6, Sweden's workforce is largely centered around the tertiary/services sectors, with Health and social work, Education, and Public administration and defence; compulsory social security employing 31. Other industries that are deeply linked include Other business activities, Construction, and Wholesale trade. Sweden is the only country where Health and social work has the highest forward and backward employment linkage coefficients. This is likely because of strong state support for healthcare and other public services in Sweden. Health expenditure as a percentage of GDP is 11.9% in Sweden, which is the second-highest out of all OECD countries apart from the USA, which spends 17.1% of GDP on healthcare ( [131] World Bank, 2017), although that spending is of a different nature. As can be seen in Figure 6, Sweden's workforce is largely centered around the tertiary/services sectors, with Health and social work, Education, and Public administration and defence; compulsory social security employing 31. Other industries that are deeply linked include Other business activities, Construction, and Wholesale trade. Sweden is the only country where Health and social work has the highest forward and backward employment linkage coefficients. This is likely because of strong state support for healthcare and other public services in Sweden. Health expenditure as a percentage of GDP is 11.9% in Sweden, which is the second-highest out of all OECD countries apart from the USA, which spends 17. In stark contrast to Sweden, as well as to the other countries analyzed, China's labour force is largely focused on the primary and secondary production and manufacturing sectors (Figure 7). China's primary and secondary sectors employ 67.6% of its workforce, compared to 25.1% in Sweden ( [141] Tukker et al., 2013). Of note are the strong backward linkages in meat production and processing in China. Production of meat pigs has a backward employment linkage of 9.27 due primarily to it drawing inputs from employment-heavy sectors such as Pigs farming; Wholesale trade; and Cultivation of vegetables, fruit, nuts. China's uniqueness among the other nations included in this study is clearly identifiable in these results and will be further elaborated upon in the following sections of this study. In stark contrast to Sweden, as well as to the other countries analyzed, China's labour force is largely focused on the primary and secondary production and manufacturing sectors (Figure 7). China's primary and secondary sectors employ 67.6% of its workforce, compared to 25.1% in Sweden ( [141] Tukker et al., 2013). Of note are the strong backward linkages in meat production and processing in China. Production of meat pigs has a backward employment linkage of 9.27 due primarily to it drawing inputs from employment-heavy sectors such as Pigs farming; Wholesale trade; and Cultivation of vegetables, fruit, nuts. China's uniqueness among the other nations included in this study is clearly identifiable in these results and will be further elaborated upon in the following sections of this study.    Table 3 below shows the results of the ELECTRE III calculations. The five most and least sustainable sectors in each country according to the six linkage criteria are shown. They are ranked according to how many other sectors in the economy that they dominate with a cutoff proportion of 0.8. The full rankings for all 163 sectors can be found in Appendix D.

Sector Sustainability Rankings
The results of the MCDA show many similarities between the six countries, as well as some prominent differences. Education is in the top three sustainable sectors for all six countries. Health and social work is in the top three for all countries except for China. Financial intermediation appears in the top five for all countries except for China. China's top five most sustainable sectors are mostly comprised of primary and secondary industries due to the concentration of its final demand and labour force in those portions of the economy. Of note is the peculiar presence of both Mining of coal and lignite; extraction of peat and Processing of meat pigs in the top five most sustainable sectors in France. While these results seem erroneous at first, they are explained by unexpectedly high backward linkage coefficients to key final demand and employment sectors. In the case of Mining of coal and lignite; extraction of peat, the sector has strong backward linkages with (i.e., it draws heavily on for inputs) final-demand and employment heavy industries such as Other business activities; Financial intermediation, except insurance and pension funding; and Computer and related activities. Its forward and backward CO 2 emissions linkages are both practically negligible, therefore it is considered much more sustainable, as calculated using this dataset, than it would seem at first glance. Processing of meat pigs has very strong backward linkages to Other business activities and Wholesale trade, which explain its high ranking in France's sustainability hierarchy. The EU's Common Agricultural Policy subsidy program could also play a role in this result. The possibility exists that these unexpected results are due to some degree of empirical inaccuracy in the data; however, the fact that their presence can be explained clearly by linkage coefficients is promising for this method's capability of eliciting and justifying unexpected and potentially valuable new insights.
The least sustainable sector results also share many similarities across the six countries. Production of electricity by gas and coal appear frequently, as would be expected due to their high CO 2 emissions intensities. China's results were more similar to those of the other five countries than they were for the top five most sustainable sectors, however the presence of Collection, purification and distribution of water on the list of least sustainable sectors is notable. This is due to it drawing large amounts of input from Production of electricity by coal and Steam and hot water supply, which are both highly CO 2 -intensive in China. In France, both Air transport and Sea and coastal water transport are relatively unsustainable due to their very high CO 2 emissions forward and backward linkages and the sectors' relatively insignificant effects on final demand and employment. In Sweden, Production of electricity by biomass and waste is highly unsustainable due to that same set of characteristics.

Differing Economic Structures of the Six Countries
The results of the above analysis reveal as many similarities between the six countries as they do differences. Many of the countries share key sustainable sectors, and the implications of any differences in economic structure can be drawn by comparing the five highly-developed, western countries (Austria, France, Germany, Sweden, and the USA) to China, which is still rapidly developing and has a greater economic focus on its primary and secondary industries. Sectors appearing at or near the top of the MCDA hierarchies for most of the five western countries include Health and social work; Education; Financial intermediation, except insurance and pension funding; Insurance and pension funding, except compulsory social security; Hotels and restaurants; Computer and related activities; and Other service activities.
These sectors contribute greatly to final demand and therefore economic growth while employing the largest portions of the countries' workforces. Sectors appearing most frequently at the bottom of the MCDA hierarchies in these countries are generally those associated with electricity production using fossil fuels and extractive industries, particularly mining of metals and ores. Air transport is also quite unsustainable in these countries due to that industry's enormous usage of fossil fuels which result in high amounts of CO 2 emissions. By comparison, China's most sustainable sectors contain some of the tertiary industries found at the top of the MCDA in the five western countries, however agricultural and other primary/secondary sectors play more important roles in China, comparatively. The most sustainable sectors in China include Education; Public administration and defence; compulsory social security; Fishing, operating of fish hatcheries and fish farms; service activities incidental to fishing; Cultivation of vegetables, fruit, nuts; Processing of Food products nec; and Manufacture of fish products, many of which are primary and secondary sectors. The tertiary sectors only account for 37.2% of China's total final demand and 32.4% China's of total employment. This is about half of the equivalent figure in the other five countries, on average. A breakdown of the broad final demand and employment structures of the six countries can be seen in Figure 10. Of note is China's heavy employment focus on its primary agricultural/extractive industries, which comprise 42.1% of total employment, although they contribute only 6.0% of final demand.
Public administration and defence; compulsory social security; Fishing, operating of fish hatcheries and fish farms; service activities incidental to fishing; Cultivation of vegetables, fruit, nuts; Processing of Food products nec; and Manufacture of fish products, many of which are primary and secondary sectors. The tertiary sectors only account for 37.2% of China's total final demand and 32.4% China's of total employment. This is about half of the equivalent figure in the other five countries, on average. A breakdown of the broad final demand and employment structures of the six countries can be seen in Figure 10. Of note is China's heavy employment focus on its primary agricultural/extractive industries, which comprise 42.1% of total employment, although they contribute only 6.0% of final demand.

Policy Implications
The insights gained from this study could be highly useful to policymakers who are formulating green economy and investment policies. First, it can offer guidance on where to focus the greening efforts by identifying sectors that are either clearly unsustainable, or are highly economically beneficial but do not have strong linkages with employment and are particularly harmful in their indirect effects on the environment. A list of such 'pitfall' industries is shown below in Table 4, which shows the list of sectors that have a strongly positive impact on the economy in terms of final demand, but are not particularly beneficial in terms of employment and have above-average upstream and downstream CO2 emissions linkages. These are distinguished from the most unsustainable sectors, as many of those tend not to be economically-attractive sectors.

Policy Implications
The insights gained from this study could be highly useful to policymakers who are formulating green economy and investment policies. First, it can offer guidance on where to focus the greening efforts by identifying sectors that are either clearly unsustainable, or are highly economically beneficial but do not have strong linkages with employment and are particularly harmful in their indirect effects on the environment. A list of such 'pitfall' industries is shown below in Table 4, which shows the list of sectors that have a strongly positive impact on the economy in terms of final demand, but are not particularly beneficial in terms of employment and have above-average upstream and downstream CO 2 emissions linkages. These are distinguished from the most unsustainable sectors, as many of those tend not to be economically-attractive sectors. Actionable steps that can be taken by policymakers from the results of this study include designing or re-designing economic development and investment plans around the sectors that are the most and least sustainable. The methodology used is flexible and could evolve continually to incorporate new data and shifting objectives, which is important considering sustainable development strategy should be highly dynamic and adaptable ( [145] El-Erian and Spence, 2008).
Each of the six countries has ratified the 2015 Paris Agreement. In addition to this shared objective, they also have clearly defined sustainable development strategies, which could benefit to some degree from insight gained using this study's methodology. The Austrian government has long displayed its commitment to environmental protection through successful pollution reduction initiatives, relatively high environmental spending as a proportion of GDP, and a commitment to future green growth through its 'Masterplan Green Jobs' and 'Growth in Transition' initiatives ([146] OECD, 2013). Its policy support for renewable energy is strong with 100% of electricity in its largest state, Lower Austria, now being generated from renewables ( [147] France-Presse, 2015). China has historically been one of the most environmentally-impactful nations on earth due to severe air pollution resulting from industrial activity ( [148] Chan and Yao, 2008; [149] Feng, 1999). However, its government has adopted more environmentally-friendly policies in recent years, with renewable energy near the forefront of that focus ( However, France still relies heavily on nuclear energy, although that reliance is falling. Its percentage of electricity generated from nuclear come down from 78.1% in 2006 to 72.3% in 2016 ( [132] World Nuclear Association, 2017). Germany is highly ambitious in its plans to decarbonize its economy, particularly as it relates to renewable energy policy. Ultimately, it hopes to reduce its total GHG emissions by 80% to 95% by 2050 as per its 'Climate Action Plan 2050' ([134] Government of Germany, 2016). At present, Germany' economy is highly CO 2 -intensive and there is much potential for improvement which could be streamlined by identifying and growing key sustainable sectors. Sweden is perhaps the most environmentally successful of the six countries due to its government's highly ambitious policy support for sustainability, which has resulted in drastic GHG emissions reductions in recent decades ([146] OECD, 2013). Its proximity to the Baltic Sea and the issues surrounding overfishing in that region will continue to be a primary concern for Sweden in the future ([154] Lindegren et al., 2009). The USA currently faces major challenges to its policy support for sustainable development due actions by its current federal government that include proposed funding cuts to its Environmental Protection Agency ([155] Neslen, 2017) and efforts to increase domestic coal production and exports ( [144] U.S. Energy Information Administration, 2017). It has also signaled its intention to withdraw from the Paris Agreement ([83] Thomas, 2017).
In spite of this, some individual states are pursuing their own ambitious sustainability and renewable energy policy targets. For example, California's "2030 Climate Commitment" aims to generate half of the state's electricity from renewables by 2030 ( [156] State of California Energy Commission, 2015). The state of Oregon has also legislated a requirement for 50% of its electricity to be derived from renewables by 2040 ([157] Oregon Department of Energy, 2016).

Conclusions
In this study, EE I-O and MCDA have been employed to identify the most sustainable sectors out of a total of 163 sectors in six countries (Austria, China, France, Germany, Sweden, and the USA) according to their economic, environmental, and social impact. Specifically, forward and backward linkages based on the Leontief Inverse Matrices of the I-O tables were weighted by final demand, CO 2 emissions, and number of employees to determine those sectors that had above average upstream and downstream impacts on the economy as a whole. The linkages for each country were then used as criteria in an MCDA calculation using the ELECTRE III method. This process created a hierarchy of dominant relationships between sectors that showed which sectors were most optimal in maximizing final demand and number of employees, whilst reasonably minimizing CO 2 emissions. The results for the six countries reflected the underlying fundamental differences in the structure and nature of these economies, and these differences are contrasted and assessed for their implications and significance to policymakers. Sectors that were most frequently identified as being highly sustainable in most or all of the six countries included Education, Health and social work, and Hotels and restaurants.
The least sustainable sectors included Production of electricity by gas, coal petroleum and other oil derivatives; mining of metals and other ores; Manufacture of cement, lime and plaster; and Air transport. Some sectors that were sustainable in some countries were highly unsustainable in others, and vice versa. Examples of this include Cultivation of crops not elsewhere classified, which is far more unsustainable in France than it is on average for the other countries; Financial intermediation, except insurance and pension funding, which is far more unsustainable in China than it is on average for the other countries; and Mining of aluminum ores and concentrates, which is far more sustainable in France than it is on average for the other countries. These results prove that there is no effective one-size-fits-all policy strategy for sustainable development that applies to all countries. Taking these coefficients into account can help individual nations optimize their pursuit of sustainable development so that collective international targets such as the Paris Climate Agreement can be met in the most efficient and effective way possible.

Conflicts of Interest:
The authors declare no conflict of interest.

Appendix C. Linkage Coefficient Trade-Off Charts
Figures A4-A7 show the trade-offs between linkage coefficients for final demand, CO 2 emissions, and employment for each of the six countries observed in this study. This is intended as an alternative way to visualize the EE I-O key sector analysis results. The average of the forward and backwards coefficients was used for each data point. The average CO 2 linkages and final demand linkages are shown on each x-and y-axis, respectively, and the size of the circles encodes the average employment linkage. The most sustainable sectors tend to coalesce at the top-left quadrant of each graph, and are those with relatively high average employment linkage coefficients. Figures A4-A7 show the trade-offs between linkage coefficients for final demand, CO2 emissions, and employment for each of t observed in this study. This is intended as an alternative way to visualize the EE I-O key sector analysis results. The average of t backwards coefficients was used for each data point. The average CO2 linkages and final demand linkages are shown on each respectively, and the size of the circles encodes the average employment linkage. The most sustainable sectors tend to coalesc quadrant of each graph, and are those with relatively high average employment linkage coefficients.