An Urban Metabolism and Carbon Footprint Analysis of the Jing–Jin–Ji Regional Agglomeration

Urban energy metabolism includes processes for exploiting, transforming, and consuming energy, as well as processes for recycling by‐products and wastes. Embodied energy is the energy consumed during all of these activities, both directly and indirectly. Multiregional input‐output (MRIO) analysis can calculate the energy consumption embodied in flows among sectors for multiple cities or regions. Our goal was to address a problem apparent in previous research, which was insufficient attention to indirect energy flows. We combined MRIO analysis with ecological network analysis to calculate the embodied energy consumption and the energy‐related carbon footprints of five sectors in three regions that comprise the Jing‐Jin‐Ji agglomeration, using data from 2002 and 2007. Our analysis traced metabolic processes of sectors from the perspective of final consumption. Based on the embodied energy analysis, we quantified the indirect energy consumption implied in exchanges of sectors and its distribution and identified the relationships formed through the indirect consumption to analyze the roles of providers and receivers in the system. Results showed that the embodied energy consumption for the Jing‐Jin‐Ji region increased from 2002 to 2007 as a result of increased energy consumption in Tianjin and Hebei. Overall, consumption of Beijing decreased likely attributable to the fact that government policies relocated industries during this time in anticipation of the Olympic Games. The relationships among sectors changed: Beijing changed from a net exporter to an importer, whereas Hebei changed from a net importer of energy from Beijing to an exporter to Beijing, and Tianjin served as an importer in both years.     

The Energy Structure of the Canadian Economy

We developed a model of a national economy in which the phenomena of supply, demand, economic growth, and international trade are represented in terms of energy flows. In examining the structure of the economy, we distinguish between the energy embodied in capital assets used in the production and distribution of energy and that embodied in capital assets and goods that consume energy. Sources used to quantify the energy flows include: end‐use energy data by economic sector; International Energy Agency–style national energy balances, and national input‐output tables. As an example, the Canadian economy for 2008 produced 16.97 exajoules (EJ) of energy, which after net export of 6.16 EJ and other adjustments left a total primary energy consumption of 10.61 EJ. The energy supply and distribution sectors used close to 32% (3.36 EJ) of total primary consumption. Analysis of primary energy consumption shows that 25.14% was embodied in household consumption, 22.85% was consumed directly by households, 7.88% was embodied in government services, and 34.07% was embodied in exports. Of significance to economic growth, 7.14% was embodied in capital in energy demanding sectors, 1.25% in energy consuming personal assets, and 1.52% in supply sector capital. The energy return on energy investment was relatively constant, averaging 5.14 between 1990 and 2008. Capital investments required to decouple the Canadian economy from its dependence on fossil fuels are discerned.     

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

As major drivers of economy, households induce a large share of worldwide environmental impacts. The variability of local consumption patterns and associated environmental impacts needs to be quantified as an important starting point to devise targeted measures aimed at reducing household environmental footprints. The goal of this article is the development and appraisal of a comprehensive regionalized bottom‐up model that assesses realistic environmental profiles for individual households in a specific region. For this purpose, a physically based building energy model, the results of an agent‐based transport simulation, and a data‐driven household consumption model were interlinked within a new probability‐based classification framework and applied to the case of Switzerland. The resulting model predicts the demands in about 400 different consumption areas for each Swiss household by considering its particular circumstances and produces a realistic picture of variability in household environmental footprints. An analysis of the model results on a municipal level reveals per‐capita income, population density, buildings' age, and household structure as possible drivers of municipal carbon footprints. While higher‐emission municipalities are located in rural areas and tend to show higher shares of older buildings, lower‐emission communities have larger proportions of families and can be found in highly populated regions by trend. However, the opposing effects of various variables observed in this analysis confirm the importance of a model that is able to capture regional distinctions. The overall model constitutes a comprehensive information base supporting policymakers in understanding consumption patterns in their region and deriving environmental strategies tailored to their specific population.     

Environmental and resource burdens associated with world biofuel production out to 2050: footprint components from carbon emissions and land use to waste arisings and water consumption

Environmental or ‘ecological’ footprints have been widely used in recent years as indicators of resource consumption and waste absorption presented in terms of biologically productive land area [in global hectares (gha)] required per capita with prevailing technology. In contrast, ‘carbon footprints’ are the amount of carbon (or carbon dioxide equivalent) emissions for such activities in units of mass or weight (like kilograms per functional unit), but can be translated into a component of the environmental footprint (on a gha basis). The carbon and environmental footprints associated with the world production of liquid biofuels have been computed for the period 2010–2050. Estimates of future global biofuel production were adopted from the 2011 International Energy Agency (IEA) ‘technology roadmap’ for transport biofuels. This suggests that, although first generation biofuels will dominate the market up to 2020, advanced or second generation biofuels might constitute some 75% of biofuel production by 2050. The overall environmental footprint was estimated to be 0.29 billion (bn) gha in 2010 and is likely to grow to around 2.57 bn gha by 2050. It was then disaggregated into various components: bioproductive land, built land, carbon emissions, embodied energy, materials and waste, transport, and water consumption. This component‐based approach has enabled the examination of the Manufactured and Natural Capital elements of the ‘four capitals’ model of sustainability quite broadly, along with specific issues (such as the linkages associated with the so‐called energy–land–water nexus). Bioproductive land use was found to exhibit the largest footprint component (a 48% share in 2050), followed by the carbon footprint (23%), embodied energy (16%), and then the water footprint (9%). Footprint components related to built land, transport and waste arisings were all found to account for an insignificant proportion to the overall environmental footprint, together amounting to only about 2%     

Labor Embodied in Trade

Global production chains carry environmental and socioeconomic impacts embodied in each traded good and service. Even though labor and energy productivities tend to be higher for domestic production in high‐income countries than those in emerging economies, this difference is significantly reduced for consumption, when including imported products to satisfy national demand. The analysis of socioeconomic and environmental aspects embodied in consumption can shed a light on the real level of productivity of an economy, as well as the effects of rising imports and offshoring. This research introduces a consumption‐based metric for productivity, in which we evaluate the loss of productivity of developed nations resulting from imports from less‐developed economies and offshoring of labor‐intensive production. We measure the labor, energy, and greenhouse gas emissions footprints in the European Union's trade with the rest of the world through a multiregional input‐output model. We confirm that the labor footprint of European imports is significantly higher than the one of exports, mainly from low‐skilled, labor‐intensive primary sectors. A high share of labor embodied in exports is commonly associated with low energy productivities in domestic industries. Hence, this reconfirms that the offshoring of production to cheaper and low‐skilled, labor‐abundant countries offsets, or even reverts, energy efficiency gains and climate‐change mitigation actions in developed countries.     

Implications of the distribution of German household environmental footprints across income groups for integrating environmental and social policy design

The distribution of German household environmental footprints (EnvFs) across income groups is analyzed by using EXIOBASE v3.6 and the consumer expenditure survey of 2013. Expenditure underreporting is corrected by using a novel method, where the expenditures are modeled as truncated normal distribution. The focus lies on carbon (CF) and material (MF) footprints, which for average German households are 9.1 ± 0.4 metric tons CO₂e and 10.9 ± 0.6 metric tons material per capita. Although the lowest‐income group has the lowest share of transportation in EnvFs, at 10.4% (CF) and 3.9% (MF), it has the highest share of electricity and utilities in EnvFs, at 39.4% (CF) and 16.7% (MF). In contrast, the highest‐income group has the highest share of transportation in EnvFs, at 20.3% (CF) and 12.4% (MF). The highest‐income group has a higher share of emissions produced overseas (38.6% vs. 34.3%) and imported resource use (69.9% vs. 66.4%) compared to the average households. When substituting 50% of imported goods with domestic ones in a counterfactual scenario, this group only decreases its CF by 2.8% and MF by 5.3%. Although incomes in Germany are distributed more equally (Gini index 0.28), the German household CF is distributed less equally (0.16). A uniform carbon tax across all sectors would be regressive (Suits index ?0.13). Hence, a revenue recycling scheme is necessary to alleviate the burden on low‐income households. The overall carbon intensity shows an inverted‐U trend due to the increasing consumption of carbon‐intensive heating for lower‐income groups, indicating a possible rebound effect for these groups. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges.     

Environmental Footprints and Scenario Analysis for Assessing the Impacts of the Agri‐Food Industry on a Regional Economy: A Case Study in Spain

The study of the environmental footprints of various sectors and industries is increasingly demanded by institutions and by society. In this context, the regional perspective is becoming particularly important, and even more so in countries such as Spain, where the autonomous communities have the primary responsibility for implementing measures to combat environmental degradation and promote sustainable development, in coordination with national strategies. Taking as a case study a Spanish region, Aragon, and significant economic sectors, including agriculture and the food industry, the aim of this work is twofold. First, we calculate the associated environmental footprints (of emissions and water) from the dual perspectives of production (local impacts) and consumption (final destination of the goods produced by the agri‐food industry). Second, through a scenarios analysis, based on a general equilibrium model designed and calibrated specifically for the region, we evaluate the environmental implications of changes in the agri‐food industry (changes in the export and import pattern, as well as in consumer behavior). This model provides a flexible approximation to the environmental impacts, controlling for a wider range of behavioral and economic interactions. Our results indicate that the agri‐food industry has a significant impact on the environment, especially on water resources, which must be responsibly managed in order to maintain the differential advantage that a regional economy can have, compared to other territories.     

可持续消费的内涵及研究进展——产业生态学视角

生产和消费是产生诸多环境问题的根本原因,而可持续生产和消费则是实现可持续发展的根本途径。基于产业生态学视角,界定了可持续消费的定义及内涵,认为可持续消费首先须符合代内公平、代际公平和资源能源永续合理利用等可持续理念;其次辨识了可持续消费研究依次经历关注消费者行为直接环境影响、关注产品和服务生命周期环境影响到关注消费者责任3个阶段;最后结合我国城市化、工业化背景,提出我国可持续消费研究应该以城市居民为重点、加强生命周期数据库建设和内注重可持续生产等建议。     

Environmental Impacts of Products: A Detailed Review of Studies

Environmental effects of economic activities are ultimately driven by consumption, via impacts of the production, use, and waste management phases of products and services ultimately consumed. Integrated product policy (IPP) addressing the life‐cycle impacts of products forms an innovative new generation of environmental policy. Yet this policy requires insight into the final consumption expenditures and related products that have the greatest life‐cycle environmental impacts. This review article brings together the conclusions of 11 studies that analyze the life‐cycle impacts of total societal consumption and the relative importance of different final consumption categories. This review addresses in general studies that were included in the project Environmental Impacts of Products (EIPRO) of the European Union (EU), which form the basis of this special issue. Unlike most studies done in the past 25 years on similar topics, the studies reviewed here covered a broad set of environmental impacts beyond just energy use or carbon dioxide (CO₂) emissions. The studies differed greatly in basic approach (extrapolating LCA data to impacts of consumption categories versus approaches based on environmentally extended input‐output (EEIO) tables), geographical region, disaggregation of final demand, data inventory used, and method of impact assessment. Nevertheless, across all studies a limited number of priorities emerged. The three main priorities, housing, transport, and food, are responsible for 70% of the environmental impacts in most categories, although covering only 55% of the final expenditure in the 25 countries that currently make up the EU. At a more detailed level, priorities are car and most probably air travel within transport, meat and dairy within food, and building structures, heating, and (electrical) energy‐using products within housing. Expenditures on clothing, communication, health care, and education are considerably less important. Given the very different approaches followed in each of the sources reviewed, this result hence must be regarded as extremely robust. Recommendations are given to harmonize and improve the methodological approaches of such analyses, for instance, with regard to modeling of imports, inclusion of capital goods, and making an explicit distinction between household and government expenditure.     

Subnational greenhouse gas and land‐based biodiversity footprints in the European Union

Insights into subnational environmental impacts and the underlying drivers are scarce, especially from a consumption‐based perspective. Here, we quantified greenhouse gas (GHG) emissions and land‐based biodiversity losses associated with final consumption in 162 regions in the European Union in 2010. For this purpose, we developed an environmentally extended multi‐regional input–output (MRIO) model with subnational European information on demand, production, and trade structures subdivided into 18 major economic sectors, while accounting for trade outside Europe. We employed subnational data on land use and national data on GHG emissions. Our results revealed within‐country differences in per capita GHG and land‐based biodiversity footprints up to factors of 3.0 and 3.5, respectively, indicating that national footprints may mask considerable subnational variability. The per capita GHG footprint increased with per capita income and income equality, whereas we did not find such responses for the per capita land‐based biodiversity footprint, reflecting that extra income is primarily spent on energy‐intensive activities. Yet, we found a shift from the domestic to the foreign part of the biodiversity footprints with rising population density and income. Because our analysis showed that most regions are already net importers of GHG emissions and biodiversity losses, we conclude that it is increasingly important to address the role of trade in national and regional policies on mitigating GHG emissions and averting further biodiversity losses, both within and outside the region itself. To further increase the policy relevance of subnational footprint analyses, we also recommend the compilation of more detailed subnational MRIO databases including harmonized environmental data.     

The influence of consumer behavior on energy,greenhouse gas,and water footprints of showering

Understanding variability in consumer behavior can provide further insights into how to effectively reduce environmental footprints related to household activities. Here, we developed a stochastic model to quantify the energy, greenhouse gas (GHG), and water consumption footprints of showering in four different countries (Australia, Switzerland, the United Kingdom, and the United States of America). We assessed the influence of two broadly distinct categories of behavior on the footprints of showering: habitual behaviors and one‐off reasoned actions. We also investigated whether changing showering behavior has a substantial impact on the associated energy, GHG, and water footprints. Our results show that the variation in environmental footprints within the countries due to differences in consumer behavior is a factor of 6–17 (95th percentile/5th percentile) depending on the country and the indicator selected. Both consumers’ reasoned actions (especially the choice of a specific heater and shower type) and habitual behaviors (length of showering in particular, are the dominant sources of footprint variability. Significant savings are achievable by making better one‐off decisions such as buying an efficient water heater and by taking shorter showers.     

A Hybrid‐Unit Energy Input‐Output Model to Evaluate Embodied Energy and Life Cycle Emissions for China's Economy

We develop a hybrid‐unit energy input‐output (I/O) model with a disaggregated electricity sector for China. The model replaces primary energy rows in monetary value, namely, coal, gas, crude oil, and renewable energy, with physical flow units in order to overcome errors associated with the proportionality assumption in environmental I/O analysis models. Model development and data use are explained and compared with other approaches in the field of environmental life cycle assessment. The model is applied to evaluate the primary energy embodied in economic output to meet Chinese final consumption for the year 2007. Direct and indirect carbon dioxide emissions intensities are determined. We find that different final demand categories pose distinctive requirements on the primary energy mix. Also, a considerable amount of energy is embodied in the supply chain of secondary industries. Embodied energy and emissions are crucial to consider for policy development in China based on consumption, rather than production. Consumption‐based policies will likely play a more important role in China when per capita income levels have reached those of western countries.     

Estimating Corporate Carbon Footprints with Externally Available Data

Corporate carbon footprints (CCFs) are a core tool in greenhouse gas emissions reporting. Established approaches for CCF calculation are based on an internal perspective that requires detailed corporate information. However, many firms do not publish information about their emissions. We seek to close this data gap by estimating scope 1 and 2 CCFs from an external perspective. The study uses a regression analysis approach, using actual firm‐internally computed CCFs to assess their degree of predictability from the outside. Data were collected from 93 European companies belonging to the chemicals, construction and engineering, and industrial machinery sectors. As predictors, we use five measures that are computed with publicly available corporate data: firm size; level of vertical integration; capital intensity; centrality of production; and carbon intensity of the national energy mix. The analysis shows that significant explanatory power for the CCF can be observed for size, capital intensity, and centrality of production. The best estimation results are achieved when data from different sectors are integrated into a comprehensive all‐sector model, while accounting for sector‐specific emission intensities by means of dummy variables. With an adjusted R² value of 0.817, the proposed procedure estimates CCFs in an accurate, yet also efficient, manner. Moreover, the study enhances trust in the current CCF calculation practices by showing that their results are plausible from a third‐party perspective.     

Investigating Reasons for Differences in the Results of Environmental,Physical, and Hybrid Input‐Output Models

It is vital to find reasons for differences in the results of environmental input‐output (EIO), physical input‐output (PIO), and hybrid input‐output (HIO) models for industrial and environmental policy analysis. Using EIO, PIO, and HIO models, China's industrial metabolism is calculated. Four reasons were found to account for differences in the results of analysis using EIO, PIO, and HIO models: the manner in which they deal with residential consumption, service sectors, and waste recycling, and the assumption of unique sector prices. The HIO model, which treats residential consumption as sectors of the intermediate delivery matrix, is preferred to the EIO and PIO models for analyzing industrial and environmental policies. Moreover, waste recycling in five sectors—agriculture; the manufacture of paper, printing, and articles for culture, education, and sports activities; the manufacture of nonmetallic mineral products; smelting and pressing of metals; and construction—should be comprehensively considered when using the HIO model to study problems related to these five sectors. Improvements in the EIO, PIO, and HIO models and future work are also discussed.     

Energy Cost of Living and Associated Pollution for Beijing Residents

China's remarkable economic growth in the last 3 decades has brought about big improvements in quality of life while simultaneously contributing to serious environmental problems. The aim of all economic activities is, ultimately, to provide the population with products and services. Analyzing environmental impacts of consumption can be valuable for illuminating underlying drivers for energy use and emissions in society. This study applies an environmentally extended input‐output analysis to estimate household environmental impact (HEI) of urban Beijing households at different levels of development. The analysis covers direct and indirect energy use and emissions of carbon dioxide (CO₂), sulfur dioxide (SO₂), and nitrogen oxide (NO_x). On the basis of observations of how HEI varies across income groups, prospects for near‐future changes in HEI are discussed. Results indicate that in 2007, an urban resident in Beijing used, on average, 52 gigajoules of total primary energy supply. The corresponding annual emissions were 4.2 tonnes CO₂, 27 kilograms SO₂, and 17 kilograms NO_x. Of this, only 18% to 34% was used or emitted by the households directly. While the overall expenditure elasticity of energy use is around 0.9, there is a higher elasticity of energy use associated with transport. The results suggest that significant growth in HEI can be expected in the near future, even with substantial energy efficiency improvements.     

Demand for biomass to meet renewable energy targets in the United States: implications for land use

Renewable energy policies in the electricity and transportation sectors in the United States are expected to create demand for biomass and food crops (corn) that could divert land from food crop production. We develop a dynamic, open‐economy, price‐endogenous multi‐market model of the US agricultural, electricity and transportation sectors to endogenously determine the quantity and mix of bioenergy likely to be required to meet the state Renewable Portfolio Standards (RPSs) and the federal Renewable Fuel Standard (RFS) if implemented independently or jointly (RFS & RPS) over the 2007–2030 period and their implications for the extent and spatial pattern of diversion of land from other uses for biomass feedstock production. We find that the demand for biomass ranges from 100 million metric tons (MMT) under the RPS alone to 310 MMT under the RFS & RPS; 70% of the biomass in the latter case can be met by crop and forest residues, while the rest can be met by devoting 3% of cropland to energy crop production with 80% of this being marginal land. Our findings show significant potential to meet current renewable energy goals by expanding high‐yielding energy crop production on marginal land and using residues without conflicting with food crop production.     

The Importance of Imports for Household Environmental Impacts

A promising way to reduce environmental impacts of consumer expenditure is through the encouragement of more sustainable consumption patterns. Consumers cause environmental impacts both directly, such as by fuel use in personal cars, and indirectly, by paying for the production of consumables. With increased international trade, the indirect environmental impacts are difficult to determine because a portion of the emissions occurs in different geographical regions. Many previous studies have unrealistically assumed that imports are produced using domestic production technology. For countries with diverging technology and energy mixes the likely errors are significant. This study applies a methodology that explicitly includes technology differences to the case of Norwegian households. It is found that a significant portion of pollution is embodied in Norwegian household imports. Further, a disproportionately large amount of pollution is embodied in imports from developing countries. Overall, as in previous studies, we find that mobility and food are most important in terms of household environmental impacts. By analyzing the imports in more detail we find that for some sectors the majority of emissions occur in foreign regions; in particular, this is true for food, business services, clothing, chemicals, furniture, cars, agriculture, textiles, and most manufactured products.     

Resource Use and Waste Generation by the Tourism Industry on the Big Island of Hawaii

A survey of the tourism industry on the island of Hawaii (the Big Island) in the state of Hawaii in the United States was conducted to collect baseline information on major resources (energy, food, and water consumption) and waste generation from five tourism sectors: accommodations, food and beverages (restaurants), golf courses, tourism services (tours), and rental cars. The questionnaire was developed and 50 establishments from the target sectors participated in this survey. Resource consumption and waste generation were calculated by the number of establishments, employees, and visitors. Using these factors and island‐wide statistics (the number of establishments, job counts, and visitors), this study estimated the current status of island‐wide water, food, and energy consumption and waste generation by these five sectors of the tourism industry. The estimate shows that the tourism sectors surveyed for this study account for 21.7% of the island's total energy consumption, 44.7% of the island‐wide water consumption, and 10.7% of the island‐wide waste generation. Using a per guest emission factor (such as per employee, guest room, and seat) provided in this study, the owners and managers of tourism establishments can calculate a baseline for each resource input and output. This is essential information to improve the industry's efficiency and result in economic savings.     

Developing the Chinese Environmentally Extended Input‐Output (CEEIO) Database

Environmentally extended input‐output (EEIO) databases are increasingly used to examine environmental footprints of economic activities. Studies focusing on China have independently, repeatedly developed EEIO databases for China. These databases are usually not publicly available, leading to repeated efforts, inconsistent with one another using different approaches, of limited environmental accounts, and lacking transparency, preventing continuous updating. We developed a transparent, comprehensive, and consistent Chinese EEIO database covering a wide period of time (currently 1992, 1997, 2002, and 2007 for which benchmark input‐output tables [IOTs] are available), sector classifications (original sector classifications in benchmark IOTs, a 45‐sector classification commonly used in China's environmental and energy statistics, and a 91‐sector classification with maximized sector resolution ensuring temporal consistence), and environmental satellite accounts for 256 types of resources and 30 types of pollutants in this study. Moreover, the environmental satellite accounts cover households in addition to sectors, allowing developing closed models. We make this database publicly available with open access for broader dissemination ( www.ceeio.com ). We demonstrate the database by evaluating environmental pressures of Chinese products in 2007. Comparisons of our database with previous studies validate its rationality and reliability.     

Environmental Impact Assessment of Wood Use in Japan through 2050 Using Material Flow Analysis and Life Cycle Assessment

In this study, we used material flow analysis and life cycle assessment to quantify the environmental impacts and impact reductions related to wood consumption in Japan from 1970 to 2013. We then conducted future projections of the impacts and reductions until 2050 based on multiple future scenarios of domestic forestry, wood, and energy use. An impact assessment method involving characterization, damage assessment, and integration with a monetary unit was used, and the results were expressed in Japanese yen (JPY). We found that environmental impacts from paper consumption, such as climate change and urban air pollution, were significant and accounted for 56% to 83% of the total environmental impacts between 1970 and 2013. Therefore, reductions of greenhouse gas, nitrogen oxide, and sulfur oxide emissions from paper production would be an effective measure to reduce the overall environmental impacts. An increase in wood use for building construction, civil engineering, furniture materials, and energy production could lead to reductions of environmental impacts (via carbon storage, material substitution, and fuel substitution) amounting to 357 billion JPY in 2050, which is equivalent to 168% of the 2013 levels. Particularly, substitution of nonwooden materials, such as cement, concrete, and steel, with wood products in building construction could significantly contribute to impact reductions. Although an increase of wood consumption could reduce environmental impacts, such as climate change, resource consumption, and urban air pollution, increased wood consumption would also be associated with land‐use impacts. Therefore, minimizing land transformations from forest to barren land will be important.