Increasing Precision in Greenhouse Gas Accounting Using Real‐Time Emission Factors

For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real‐time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real‐time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.     

Weight under Steel Wheels: Material Stock and Flow Analysis of High‐Speed Rail in China

The construction of a nation‐wide high‐speed rail (HSR) network has emerged as a hugely expensive and ambitious infrastructure project in China. As of December 2012, some 8,800 kilometers (km) of double‐track HSR lines came into service in the country, accounting for 40% of the total HSR length in the world. The network is expected to expand to 34,000 km or longer in around two decades. As the first HSR system specially built and operated in an economically developing country, it helps integrate the sprawling economy and lift the quality of life of the increasing urban population. China's experiences in HSR are expected to be of value to other countries aiming to adopt bullet train systems, especially those at a similar level of industrialization and urbanization. This work specifically examines material stocks and flows associated with the HSR infrastructure construction in China. A major distinction from the construction of HSR tracks in Europe is that nearly 70% of the HSR tracks in China are laid upon bridges or inside tunnels, which are structures that demand great amounts of raw materials. The entire network, once completed by 2030, will cumulatively require 83 to 137 million tonnes (Mt) of steel and 560 to 920 Mt of cement. This is still a small share of China's use of material resources. Nonetheless, the massive application of the steel‐ and cement‐intensive structures deserves consideration when assessing the environmental performance of HSR over its entire life cycle.     

Effects of Using Heterogeneous Prices on the Allocation of Impacts from Electricity Use: A Mixed‐Unit Input‐Output Approach

Economic input‐output life cycle assessment (IO‐LCA) models allow for quick estimation of economy‐wide greenhouse gas (GHG) emissions associated with goods and services. IO‐LCA models are usually built using economic accounts and differ from most process‐based models in their use of economic transactions, rather than physical flows, as the drivers of supply‐chain GHG emissions. GHG emissions estimates associated with input supply chains are influenced by the price paid by consumers when the relative prices between individual consumers are different. We investigate the significance of the allocation of GHG emissions based on monetary versus physical units by carrying out a case study of the U.S. electricity sector. We create parallel monetary and mixed‐unit IO‐LCA models using the 2007 Benchmark Accounts of the U.S. economy and sector specific prices for different end users of electricity. This approach is well suited for electricity generation because electricity consumption contributes a significant share of emissions for most processes, and the range of prices paid by electricity consumers allows us to explore the effects of price on allocation of emissions. We find that, in general, monetary input‐output models assign fewer emissions per kilowatt to electricity used by industrial sectors than to electricity used by households and service sectors, attributable to the relatively higher prices paid by households and service sectors. This fact introduces a challenging question of what is the best basis for allocating the emissions from electricity generation given the different uses of electricity by consumers and the wide variability of electricity pricing.     

Data for the Carbon Footprinting of Rendering Operations

This article presents a tool and data for calculation of the carbon footprint of rendering operations in North America, quantifying Scope 1 (direct) and Scope 2 (indirect) greenhouse gas emissions. Scope 3 (life cycle) emissions are not included. According to the sample data, in one year an average‐size rendering plant in North America processes 100,000 tonnes (t) of meat by‐products, fallen animals, and restaurant grease and produces 40,000 t of marketable fats and proteins. A plant of this size emits directly about 20,000 t of carbon dioxide (CO₂), mostly by burning fuels to operate cookers that destroy pathogens, drive off moisture, and separate the fat and protein. Another 4,000 t of CO₂ is emitted by utility companies to provide electricity for the rendering process. These direct and indirect emissions are equivalent to about 30% of the CO₂ that would be released if all of the carbon in the rendered raw material were decomposed into CO₂.     

Hour‐by‐Hour Analysis for Increased Accuracy of Greenhouse Gas Emissions for a Low‐Energy Condominium Design

The seasonal and hourly variation of electricity grid emissions and building operational energy use are generally not accounted for in carbon footprint analyses of buildings. This work presents a technique for and results of such an analysis and quantifies the errors that can be encountered when these variations are not appropriately addressed. The study consists of an hour‐by‐hour analysis of the energy used by four different variations of a five‐story condominium building, with a gross floor area of approximately 9,290 square meters (m²), planned for construction in Markham, Ontario, Canada. The results of the case studied indicate that failure to account for variation can, for example, cause a 4% error in the carbon footprint of a building where ground source heat pumps are used and a 6% and 8% error in accounting for the carbon savings of wind and photovoltaic systems, respectively. After the building envelope was enhanced and sources of alternative energy were incorporated, the embodied greenhouse gas (GHG) emissions were more than 50% of the building's operational emissions. This work illustrates the importance of short‐time‐scale GHG analysis for buildings.     

Empirical Fuel Consumption and CO2 Emissions of Plug‐In Hybrid Electric Vehicles

Plug‐in hybrid electric vehicles (PHEVs) combine electric and conventional propulsion. Official fuel consumption values of PHEVs are based on standardized driving cycles, which show a growing discrepancy with real‐world fuel consumption. However, no comprehensive empirical results on PHEV fuel consumption are available, and the discrepancy between driving cycle and empirical fuel consumption has been conjectured to be large for PHEV. Here, we analyze real‐world fuel consumption data from 2,005 individual PHEVs of five PHEV models and observe large variations in individual fuel consumption with deviation from test‐cycle values in the range of 2% to 120% for PHEV model averages. Deviations are larger for short‐ranged PHEVs. Among others, range and vehicle power are influencing factors for PHEV model fuel consumption with average direct carbon dioxide (CO₂) emissions decreasing by 2% to 3% per additional kilometer (km) of electric range. Additional simulations show that PHEVs recharged from renewable electricity can noteworthily reduce well‐to‐wheel CO₂ emissions of passenger cars, but electric ranges should not exceed 200 to 300 km since battery production is CO₂‐intense. Our findings indicate that regulations should (1) be based on real‐world fuel consumption measurements for PHEV, (2) take into account charging behavior and annual mileages, and (3) incentivize long‐ranged PHEV.     

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.     

Life Cycle Assessment of End‐of‐Life Management Options for Construction and Demolition Debris

A life cycle assessment (LCA) of various end‐of‐life management options for construction and demolition (C&D) debris was conducted using the U.S. Environmental Protection Agency's Municipal Solid Waste Decision Support Tool. A comparative LCA evaluated seven different management scenarios using the annual production of C&D debris in New Hampshire as the functional unit. Each scenario encompassed C&D debris transport, processing, separation, and recycling, as well as varying end‐of‐life management options for the C&D debris (e.g., combustion to generate electricity versus landfilling for the wood debris stream and recycling versus landfilling for the nonwood debris stream) and different bases for the electricity generation offsets (e.g., the northeastern U.S. power grid versus coal‐fired power generation). A sensitivity analysis was also conducted by varying the energy content of the C&D wood debris and by examining the impact of basing the energy offsets on electricity generated from various fossil fuels. The results include impacts for greenhouse gas (GHG) emissions, criteria air pollutants, ancillary solid waste production, and organic and inorganic constituents in water emissions. Scenarios with nonwood C&D debris recycling coupled with combustion of C&D wood debris to generate electricity had lower impacts than other scenarios. The nonwood C&D debris recycling scenarios where C&D wood debris was landfilled resulted in less overall impact than the scenarios where all C&D debris was landfilled. The lowest impact scenario included nonwood C&D debris recycling with local combustion of the C&D wood debris to generate electricity, providing a net gain in energy production of more than 7 trillion British thermal units (BTU) per year and a 130,000 tons per year reduction in GHG emissions. The sensitivity analysis revealed that for energy consumption, the model is sensitive to the energy content of the C&D wood debris but insensitive to the basis for the energy offset, and the opposite is true for GHG emissions.     

Unintentional Flow of Alloying Elements in Steel during Recycling of End‐of‐Life Vehicles

Alloying elements in steel add a wide range of valuable properties to steel materials that are indispensable for the global economy. However, they are likely to be effectively irretrievably blended into the steel when recycled because of (among other issues) the lack of information about the composition of the scrap. This results in the alloying elements dissipating in slag during steelmaking and/or becoming contaminants in secondary steel. We used the waste input‐output material flow analysis model to quantify the unintentional flows of alloying elements (i.e., chromium, nickel, and molybdenum) that occur in steel materials and that result from mixing during end‐of‐life (EOL) processes. The model can be used to predict in detail the flows of ferrous materials in various phases, including the recycling phase by extending steel, alloying element source, and iron and steel scrap sectors. Application of the model to Japanese data indicates the critical importance of the recycling of EOL vehicles (ELVs) in Japan because passenger cars are the final destination of the largest share of these alloying elements. However, the contents of alloying elements are rarely considered in current ELV recycling. Consequently, the present study demonstrates that considerable amounts of alloying elements, which correspond to 7% to 8% of the annual consumption in electric arc furnace (EAF) steelmaking, are unintentionally introduced into EAFs. This result suggests the importance of quality‐based scrap recycling for efficient management of alloying elements.     

A life‐cycle assessment model for zero emission neighborhoods

Buildings represent a critical piece of a low‐carbon future, and their long lifetime necessitates urgent adoption of state‐of‐the‐art performance standards to avoid significant lock‐in risk regarding long‐lasting technology solution choices. Buildings, mobility, and energy systems are closely linked, and assessing their nexus by aiming for Zero Emission Neighborhoods (ZENs) provides a unique chance to contribute to climate change mitigation. We conducted a life‐cycle assessment of a Norwegian ZEN and designed four scenarios to test the influence of the house size, household size, and energy used and produced in the buildings as well as mobility patterns. We ran our scenarios with different levels of decarbonization of the electricity mix over a period of 60 years. Our results show the importance of the operational phases of both the buildings and mobility in the neighborhood's construction, and its decline over time induced by the decarbonization of the electricity mix. At the neighborhood end‐of‐life, embodied emissions then become responsible for the majority of the emissions when the electricity mix is decarbonized. The choice of functional unit is decisive, and we thus argue for the use of a primary functional unit “per neighborhood,” and a second “per person.” The use of a “per m²” functional unit is misleading as it does not give credits to the precautionary use of floor area. To best mitigate climate change, climate‐positive behaviors should be combined with energy efficiency standards that incorporate embodied energy, and absolute threshold should be combined with behavioral changes.     

Evaluating the Potential for Harmonized Prediction and Comparison of Disposal‐Stage Greenhouse Gas Emissions for Biomaterial Products

The carbon footprint (CF) of biofuels and biomaterials is a barrier to their acceptance, yet the greenhouse gas emissions associated with disposing of biomaterials are frequently omitted from analyses. This article investigates whether harmonization is appropriate for calculating the importance of biomaterials’ disposal. This research shows that disposal stages could double a biomaterial's CF, or reduce it to the point that it could claim to be zero carbon. Incineration with combined heat and power coupled with on‐site energy production in the biorefinery are identified as prerequisites to being zero carbon. The article assesses the current UK waste infrastructure's ability to support a low‐carbon bio‐based future economy, and finds that presently it only achieves marginal net reductions when compared to landfill and so cannot be said to support low‐carbon biomaterials, though the article challenges the polluter pays principle where low‐carbon disposal infrastructure are not available. Reuse and recycling are shown to have the potential to offset all the emissions caused by landfill of biomaterials. However, the savings are not so great as to offset the biomaterial's upstream emissions. The study explores the ability to overcome the barriers to incorporating disposal into life cycle assessment while identifying limitations of using harmonization as an assessment method. Specifically, data availability and industry consensus are flagged as major barriers. The study also uses sensitivity analysis to investigate the influence of methodological choices, such as allowing additional reuse and recycling stages, classifying biomaterials into different types, and choosing between opposing allocation methods.     

Estimating the Greenhouse Gas Balance of Individual Gas‐Fired and Oil‐Fired Electricity Plants on a Global Scale

Life cycle greenhouse gas (LC‐GHG) emissions from electricity generated by a specific resource, such as gas and oil, are commonly reported on a country‐by‐country basis. Estimation of variability in LC‐GHG emissions of individual power plants can, however, be particularly useful to evaluate or identify appropriate environmental policy measures. Here, we developed a regression model to predict LC‐GHG emissions per kilowatt‐hour (kWh) of electricity produced by individual gas‐ and oil‐fired power plants across the world. The regression model uses power plant characteristics as predictors, including capacity, age, fuel type (fuel oil or natural gas), and technology type (single or combined cycle) of the plant. The predictive power of the model was relatively high (R² = 81% for predictions). Fuel and technology type were identified as the most important predictors. Estimated emission factors ranged from 0.45 to 1.16 kilograms carbon dioxide equivalents per kilowatt‐hour (kg CO₂‐eq/kWh) and were clearly different between natural gas combined cycle (0.45 to 0.57 kg CO₂‐eq/kWh), natural gas single cycle (0.66 to 0.85 kg CO₂‐eq/kWh), oil combined cycle power plants (0.63 to 0.79 kg CO₂‐eq/kWh), and oil single cycle (0.94 to 1.16 kg CO₂‐eq/kWh). Our results thus indicate that emission data averaged by fuel and technology type can be profitably used to estimate the emissions of individual plants.     

Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production,Part I: Analytical Framework and Baseline Results

This first article of a two‐article series describes a framework and life cycle–based model for typical almond orchard production systems for California, where more than 80% of commercial almonds on the world market are produced. The comprehensive, multiyear, life cycle–based model includes orchard establishment and removal; field operations and inputs; emissions from orchard soils; and transport and utilization of co‐products. These processes are analyzed to yield a life cycle inventory of energy use, greenhouse gas (GHG) emissions, criteria air pollutants, and direct water use from field to factory gate. Results show that 1 kilogram (kg) of raw almonds and associated co‐products of hulls, shells, and woody biomass require 35 megajoules (MJ) of energy and result in 1.6 kg carbon dioxide equivalent (CO₂‐eq) of GHG emissions. Nitrogen fertilizer and irrigation water are the dominant causes of both energy use and GHG emissions. Co‐product credits play an important role in estimating the life cycle environmental impacts attributable to almonds alone; using displacement methods results in net energy and emissions of 29 MJ and 0.9 kg CO₂‐eq/kg. The largest sources of credits are from orchard biomass and shells used in electricity generation, which are modeled as displacing average California electricity. Using economic allocation methods produces significantly different results; 1 kg of almonds is responsible for 33 MJ of energy and 1.5 kg CO₂‐eq emissions. Uncertainty analysis of important parameters and assumptions, as well as temporary carbon storage in orchard trees and soils, are explored in the second article of this two‐part article series.     

Land management and climate change determine second‐generation bioenergy potential of the US Northern Great Plains

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO₂ concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO₂ was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.     

When, where and how the bridge breaks: anaphase bridge breakage plays a crucial role in gene amplification and HSR generation

Amplified genes are frequently localized on extrachromosomal double minutes (DMs) or in chromosomal homogeneously staining regions (HSRs). We previously showed that a plasmid bearing a mammalian replication initiation region could efficiently generate DMs and HSRs after transfection into human tumor cell lines. The Breakage-Fusion-Bridge (BFB) cycle model, a classical model that explains how HSRs form, could also be used to explain how the transfected plasmids generate HSRs. The BFB cycle model involves anaphase bridge formation due to the presence of dicentric chromosomes, followed by the breakage of the bridge. In this study, we used our plasmid-based model system to analyze how anaphase bridges break during mitosis. Dual-color fluorescence in situ hybridization analyses revealed that anaphase bridges were most frequently severed in their middle irrespective of their lengths, which suggests that a structurally fragile site exists in the middle of the anaphase bridge. Breakage of the chromosomal bridges occurred prior to nuclear membrane reformation and the completion of cytokinesis, which indicates that mechanical tension rather than cytokinesis is primarily responsible for severing anaphase bridges. Time-lapse observation of living cells revealed that the bridges rapidly shrink after being severed. If HSR length was extended too far, the bridge could no longer be resolved and became tangled depending on the tension. The unbroken bridge appeared to inhibit the completion of cytokinesis. These observations strongly suggest that anaphase bridges are highly elastic and that the length of the spindle axis determines the maximal HSR length.     

Accounting for Emissions and Sinks from the Biogeochemical Cycle of Carbon in the U.S. Economic Input‐Output Model

Biogeochemical cycles are essential ecosystem services that continue to degrade as a result of human activities, but are not fully considered in efforts toward sustainable engineering. This article develops a model that integrates the carbon cycle with economic activities in the 2002 U.S. economy. Data about the carbon cycle, including emissions and sequestration flows, is obtained from the greenhouse gas inventory of the U.S. Environmental Protection Agency. Economic activities are captured by the economic input‐output model available from the Bureau of Economic Analysis. The resulting model is more comprehensive in its accounting for the carbon cycle than existing methods for carbon footprint (CF) calculations. Examples of unique flows in this model include the effect of land‐use and land‐cover change on carbon dioxide flow within the U.S. national boundary, carbon sequestration in urban trees, and emissions resulting from liming. This model is used to gain unique insight into the carbon profile of U.S. economic sectors by providing the life cycle emissions and sequestration in each sector. Such insight may be used to support policies, manage supply chains, and be used for more comprehensive CF calculations.     

Assessing the carbon footprint of the transport sector in mega cities via streamlined life cycle assessment: a case study of Shenzhen,South China

Purpose

As one of largest energy consumers, the transport sector (TS) has significant impacts on the environment. Shenzhen, a developed megacity in South China, plays a leadership role in promoting the development of energy efficient vehicles in China.

Methods

This paper aims to assess the carbon footprint (CF) of the TS in Shenzhen via a Streamlined Life Cycle Assessment method. Consequently, the current environmental performance of the TS is evaluated and improvement potentials are examined.

Results and discussion

The results show that CF has gained rapid growth over the past decade at an annual rate of 15.3 %, closely corresponding with the growth of the Gross Domestic Product (18.9 %) in Shenzhen. The total CF in 2013 was estimated as 50.7 million tons (ranging from 41.7 to 59.9). Road based freight transport accounts for the largest share of the TS’ emissions. The most significant contributors in this sector are: light duty trucks, urban public transport bus service, and passenger air transport. Meanwhile, this study took new energy vehicles into consideration in order to explore the range of CF mitigation potential in Shenzhen. The potential carbon abatement is not significant in comparison with the impact growth derived from the increasing freight and passenger transport based on the assumption that the transport intensity and its annual growth rate maintain at the current levels.

Conclusions

This study offers a useful approach to evaluate the available options for sustainable transport system planning in Shenzhen. For carbon emissions reductions from the TS, policies and technological innovations are essential to facilitate the transition to a low carbon TS. In addition, the methodology developed in this study could be used for assessing CF in other sectors.

     

Adapting Stand‐Alone Renewable Energy Technologies for the Circular Economy through Eco‐Design and Recycling

Renewable energy (RE) technologies are looked upon favorably to provide for future energy demands and reduce greenhouse gas (GHG) emissions. However, the installation of these technologies requires large quantities of finite material resources. We apply life cycle assessment to 100 years of electricity generation from three stand‐alone RE technologies—solar photovoltaics, run‐of‐river hydro, and wind—to evaluate environmental burden profiles against baseline electricity generation from fossil fuels. We then devised scenarios to incorporate circular economy (CE) improvements targeting hotspots in systems’ life cycle, specifically (1) improved recycling rates for raw materials and (ii) the application of eco‐design. Hydro presented the lowest environmental burdens per kilowatt‐hour of electricity generation compared with other RE technologies, owing to its higher efficiency and longer life spans for main components. Distinct results were observed in the environmental performance of each system based on the consideration of improved recycling rates and eco‐design. CE measures produced similar modest savings in already low GHG emissions burdens for each technology, while eco‐design specifically had the potential to provide significant savings in abiotic resource depletion. Further research to explore the full potential of CE measures for RE technologies will curtail the resource intensity of RE technologies required to mitigate climate change.     

Implementing Carbon‐Footprint‐Based Calculation Tools in Municipal Greenhouse Gas Inventories

Policy makers and practitioners working on local climate action continuously strive for improved greenhouse gas (GHG) inventories to support their mitigation strategies. The provision of such inventories, with the sufficient detail and quality needed, has proven to be challenging. This article describes the application of a carbon‐footprint–based (CF‐based) calculation tool to Norwegian municipalities. The aim is to improve the local GHG inventory, with a particular focus on the emissions resulting from the provision of services by local governments. Through selective CF analysis, we aim to illustrate the knowledge gained from implementing these tools in local climate action plans and to further contrast CF‐based calculations with more traditional production‐based accounting. Municipalities report they have revised their understanding of the most important GHG mitigation strategies after implementing this tool.     

The Nexus of Carbon,Nitrogen, and Biodiversity Impacts from Urban Metabolism

Methodology is developed for linking the urban metabolism (UM) to global environmental stresses on the carbon (C) cycle, nitrogen (N) cycle, and biodiversity loss. UM variables are systematically mapped to the drivers of carbon, nitrogen, and biodiversity impacts. Change in mean species abundance is used as metric of biodiversity loss, by adopting the dose‐response relationships from the GLOBIO model. The main biodiversity drivers related to UM included here are land‐use change (LUC) and atmospheric N deposition. The methodology is demonstrated by studying the nexus for Shanghai in 2006, based on energy and soybean consumption. Results for Shanghai show a strong nexus between C, N, and biodiversity impact due to electricity consumption and energy used in manufacturing industries and construction. Prioritization of the shift away from coal energy will therefore lead to lowering the urban growth impact on all three dimensions. Road transportation, domestic aviation, and the metal industry impact only the C footprint highly, whereas district energy impacts only biodiversity loss highly, showing a weak nexus. Among the global impacts of soybean consumption in Shanghai on biodiversity loss (due to LUC only), the highest impact occurs in Uruguay (0.52%) followed by Brazil (0.05%) and Argentina (0.02%). The local impact on biodiversity loss (i.e., within China) of soybean consumption in Shanghai is 1.03%. However, the methodology and results are limited due to the partial inclusion of drivers, a carbon footprint based on carbon dioxide emissions only, and limitations of biodiversity loss models. Potential to overcome methodological limitations is discussed.