Environmental Impacts of Capital Formation

The investment in capital goods is a well‐known driver of economic activity, associated resource use, and environmental impact. In national accounting, gross fixed capital formation (GFCF) constitutes a substantial share of the total final demand of goods and services, both in terms of monetary turnover and embodied resources. In this article, we study the structure of GFCF and the environmental impacts associated with it on a global scale, and link it to measures of development. We find that the share of GFCF as part of the total carbon footprint (CF) varies more across countries than GFCF as a share of gross domestic product (GDP). Countries in early phases of development generally tend to invest in resource‐intensive assets, primarily infrastructure and machinery, whereas wealthier countries invest in less resource‐intensive assets, such as computers, software, and services. By performing a structural decomposition analysis, we assess the relative importance of investment structure and input‐output multipliers for the difference in carbon intensity of capital assets, and find that the structure of investments plays a larger role for less‐developed countries than for developed countries. We find a relative decoupling of the CF of GFCF from GDP, but we can neither confirm nor rule out the possibility of an absolute decoupling.     

When Do Allocations and Constructs Respect Material,Energy, Financial,and Production Balances in LCA and EEIO?

Conservation of mass and energy are essential to physical accounting, just as price and market balances are essential to economic accounting. These principles guide data collection and inventory compilation in industrial ecology. The resulting balanced surveys, however, can rarely be used directly for life cycle assessment (LCA) or environmentally extended input‐output (EEIO) analysis; some modeling is necessary to recast coproductions by multifunctional activities as monofunctional unit processes (a.k.a. Leontief production functions or technical “recipes”). This modeling is done with allocations in LCA and constructs in input‐output. In this article, we ask how these models respect or perturb the balances of the original inventory. Which allocations or constructs, applied to what type of data set, have the potential to simultaneously respect its multiple physical, financial, and market balances? Our analysis builds upon the recent harmonization of allocations and constructs and the ongoing development of multilayered supply and use inventory tables. We derive the necessary and sufficient conditions for balanced models, investigate the role of data aggregation, and clarify these models' relation to system expansion. We find that none of the modeling families in LCA and EEIO are balanced in general, but special data characteristics can allow for the respect of multiple balances. An analysis of these special cases allows for clear guidance for data compilation and methods integration.     

Capital in the American carbon,energy, and material footprint

Stocks of fixed capital play a vital role in fulfilling basic human needs and facilitating industrial production. Their build‐up requires great quantities of energy and materials, and generates greenhouse gas emissions and other pollution. Capital stocks influence economic production and environmental pollution through their construction and over subsequent decades through their use. We perform an environmental footprint analysis of total consumption, capital investment, and capital consumption in the United States for 2007 and 2012. In 2012, capital consumption accounted for 13%, 19%, and 40% of total carbon, energy, and material footprints, respectively. Housing, federal defense, state and local government education and other services (including household consumption of roads), personal transport fuels, and hospitals are the consumption sectors with largest capital footprints. These sectors provide fundamental needs of shelter, transport, education, and health, underlying the importance of capital services. Endogenizing capital causes the biggest proportional increase to footprints of sectors with low environmental multipliers. This work builds upon existing input‐output models of production and consumption in the United States, and provides a capital‐inclusive database of carbon, energy, and material footprints and multipliers for 2007 and 2012. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges .     

Method for endogenizing capital in the United States Environmentally‐Extended Input‐Output model

Each year businesses, governments, and homeowners in the United States invest around one fifth of gross domestic product into the creation of capital assets such as buildings, machinery, and software to enable production and consumption. Use of capital is typically included to some extent in environmental life cycle assessments of goods and services but is not incorporated into most environmentally extended input‐output (EEIO) models, including the US Environmental Protection Agency's USEEIO. Capital assets are typically created in years prior to their use, so a challenge lies in distributing the impacts of their creation over time. In this work, a highly detailed capital flow matrix approach is followed to distribute the use of fixed capital assets to consuming industries. Data from the US Bureau of Economic Analysis's Fixed Asset Accounts is merged with its Industry Accounts data by the creation of concordance tables. Public highways and streets are partially reallocated to industries operating vehicles. The resulting capital use matrix is later combined into a modified USEEIO. “Housing” is found to be the largest consumer of fixed assets, followed by general government, fossil fuel extraction, and financial industries involved in leasing. Construction, vehicles, and machinery are mostly used by industries in the form of fixed assets. The types of fixed assets used by industries are consistent with expectations: housing is dominated by structures, transport by equipment, and information industries by intellectual property products.     

Generalized Make and Use Framework for Allocation in Life Cycle Assessment

Allocation in life cycle inventory (LCI) analysis is one of the long‐standing methodological issues in life cycle assessment (LCA). Discussion on allocation among LCA researchers has taken place almost in complete isolation from the series of closely related discussions from the 1960s in the field of input?output economics, regarding the supply and use framework. This article aims at developing a coherent mathematical framework for allocation in LCA by connecting the parallel developments of the LCA and the input?output communities. In doing so, the article shows that the partitioning method in LCA is equivalent to the industry‐technology model in input?output economics, and system expansion in LCA is equivalent to the by‐product‐technology model in input?output output economics. Furthermore, we argue that the commodity‐technology model and the by‐product‐technology model, which have been considered as two different models in input?output economics for more than 40 years, are essentially equivalent when it comes to practical applications. It is shown that the matrix‐based approach used for system expansion successfully solves the endless regression problem that has been raised in LCA literature. A numerical example is introduced to demonstrate the use of allocation models. The relationship of these approaches with consequential and attributional LCA models is also discussed.     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

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.     

A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment

This article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent‐based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision‐making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass‐to‐energy case demonstrates the approach.     

An Ecological Economic Critique of the Use of Market Information in Life Cycle Assessment Research

The rising prominence of life cycle assessment (LCA) and similar environmental accounting frameworks reflects increasing awareness of the pressing necessity of managing both for eco‐efficiency and with respect to the macroscale, environmental dimensions of the material/energy flows and emissions that underpin all economic activity. However, by relying on environmentally myopic market signals to inform evaluations of the biophysical dimensions of economic activity through the widespread use of market information (in particular, via economic allocation) in LCA, we are concerned that researchers greatly compromise the value of their work to furthering these objectives. In response to this problem, we provide a systematic critique of the use of market information in attributional LCA and present the case for an ecological‐economic approach to the execution, interpretation, and application of biophysically consistent LCA research specifically intended to elucidate the environmental dimensions of meeting human needs. We further argue that, although LCA has historically been limited to informing eco‐efficiency considerations, it can and should also be used to manage for sustainable scale, which is the first condition of sustainability.     

生态足迹的模型修正与方法改进

生态足迹是测定人类活动的资源消费需求,判明自然资产是否被过度利用的有效工具。介绍了生态足迹的基本概念和模型,简单分析基本模型存在的主要缺陷和争论,重点解析了近年来生态足迹模型在参数调整、项目计算、账户扩展等方面的演变和修正。介绍生态足迹研究的传统方法:综合法和组分法,评述了生命周期评价,基于投入产出分析,三维模型,净初级生产力,能值理论,时序分析等的方法改进。对未来的研究方向提出自己的看法,期望对我国的生态足迹研究有一定的启示作用。     

Measuring Progress towards a Circular Economy: A Monitoring Framework for Economy‐wide Material Loop Closing in the EU28

The concept of a circular economy (CE) is gaining increasing attention from policy makers, industry, and academia. There is a rapidly evolving debate on definitions, limitations, the contribution to a wider sustainability agenda, and a need for indicators to assess the effectiveness of circular economy measures at larger scales. Herein, we present a framework for a comprehensive and economy‐wide biophysical assessment of a CE, utilizing and systematically linking official statistics on resource extraction and use and waste flows in a mass‐balanced approach. This framework builds on the widely applied framework of economy‐wide material flow accounting and expands it by integrating waste flows, recycling, and downcycled materials. We propose a comprehensive set of indicators that measure the scale and circularity of total material and waste flows and their socioeconomic and ecological loop closing. We applied this framework in the context of monitoring efforts for a CE in the European Union (EU28) for the year 2014. We found that 7.4 gigatons (Gt) of materials were processed in the EU and only 0.71 Gt of them were secondary materials. The derived input socioeconomic cycling rate of materials was therefore 9.6%. Further, of the 4.8 Gt of interim output flows, 14.8% were recycled or downcycled. Based on these findings and our first efforts in assessing sensitivity of the framework, a number of improvements are deemed necessary: improved reporting of wastes, explicit modeling of societal in‐use stocks, introduction of criteria for ecological cycling, and disaggregated mass‐based indicators to evaluate environmental impacts of different materials and circularity initiatives. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges .     

基于MFA和DEA的区域经济环境效率评价-以辽宁省为例

对经济与环境效率的科学评价是实现区域可持续发展的前提。运用物质流分析将辽宁省经济系统中数据进行物质化处理,再利用改进的数据包络分析模型对环境和经济效率进行综合评价。结果表明:辽宁省物资消费不主要依赖于进口,向其它地区物质输出量大;环境效率评价的综合效率主要受规模因素影响而显著低于纯技术效率,而整体经济的综合效率却主要受纯技术效率影响而下降。第二产业比重依然偏大的产业结构特征是导致上述结果的主因。进一步改造提升传统产业,发展战略性新兴产业,提高第三产业发展水平,扩大环保规模,促进居民生活质量水平全面提升,将是辽宁省以及与之相似的资源依赖型区域可持续发展的方向。     

A Structure Comparison of two Approaches to LCA Inventory Data, Based on the MIET and ETH Databases (10 pp)

Goal and Scope This study compared two different approaches to general inventory data in LCA, one involving the process-based ETH 96 database and the other an environmentally extended Input-Output table for the US, referring to MIET (Missing Inventory Estimation Tool) 2.0. The purpose of the present paper is to highlight and explain some of the differences between the two approaches, in order to give LCA practitioners a clearer idea of the advantages and limitations of using Input-Output analysis combined with process LCA. Methods The comparison was made despite substantial differences between the two approaches, through a reduction and reclassification of the ETH process technology matrix to fit the Input-Output classification scheme and by concentrating on the structure of the processes rather than their absolute values. The structure is described in terms of the percentage of the CO2 contribution to the total emission by all processes involved in the supply chain. An input and output structure comparison was carried out between ETH 96 and MIET 2.0, to extract information about their structures. Results and Discussion The results of the study show that, despite their methodological differences, MIET 2.0 and ETH 96 show substantial similarities in their overall structures. There are also differences in the structure of the two databases, and most of them have occurred randomly, while, for certain particular sectors, the differences are rather persistent. Especially the contributions by capital goods are constantly lower in ETH 96 database and vice versa. The results imply possible systematic truncation in process LCA databases, especially for a few sectors such as capital goods. Recommendation and Perspective Hybrid analysis can overcome the problem of incompleteness in process LCA, while avoiding such disadvantages of IOA as aggregation problem.     

自然资本价值核算研究综述

目前国内外对自然资本价值核算的理论与方法尚未取得共识,无疑制约了自然资本核算在生态环境保护中积极作用的发挥。基于此,本文分别从自然资本内涵及其价值核算理论基础、国内外自然资本存量与流量核算方法实证研究、自然资本货币化价值核算的合理性等方面对已有文献进行梳理与分析,发现目前自然资本核算研究主要集中在生态产品的功能量与价值量核算而缺少生态资产货币价值量核算的研究;当前采用的生态产品价值量核算的生态系统类型系数表、本地化生态过程模型、能值当量替换三种方法在精度适用性、应用推广性和计算便捷性上各有优劣,尚待进一步的深入研究;最后对自然资本货币核算的一些争论进行了分析。通过上述研究和分析,提出如下新认识：（1）自然资本相关概念尚不统一,已经形成的认识包括：自然资本具有一定的稀缺性和使用价值,资本可分为存量资本（又称生态资产）和流量资本（又称生态系统服务）;（2）自然资本核算技术繁多,研究成果的横向比较性较差,需要通过标准化核算技术方法和建立生态环境资源数据库等措施,进一步提高核算结果的准确性和可比性;（3）相比于基于生态系统类型价值系数方法和基于非市场化货币的当量替代方法,基于本地化参数的生态系统服务过程方法更能够反映本地生态环境特征,评估分析的准确度更高,生态管理抓手更多;（4）尽管自然资本货币核算理论和实践应用仍存在一些争论,但各国政府和各类组织都在推动自然资本核算工作,自然资本的探索性核算成果已在各国生态补偿和生态损害修复等方面发挥积极作用。     

Integrating life cycle cost analysis and LCA

The private sector decision making situations which LCA addresses mustalso eventually take theeconomic consequences of alternative products or product designs into account. However, neither the internal nor external economic aspects of the decisions are within the scope of developed LCA methodology, nor are they properly addressed by existing LCA tools. This traditional separation of life cycle environmental assessment from economic analysis has limited the influence and relevance of LCA for decision-making, and left uncharacterized the important relationships and trade-offs between the economic and life cycle environmental performance of alternative product design decision scenarios. Still standard methods of LCA can and have been tightly, logically, and practically integrated with standard methods for cost accounting, life cycle cost analysis, and scenario-based economic risk modeling. The result is an ability to take both economic and environmental performance — and their tradeoff relationships — into account in product/process design decision making.     

Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25

Material stocks are an important part of the social metabolism. Owing to long service lifetimes of stocks, they not only shape resource flows during construction, but also during use, maintenance, and at the end of their useful lifetime. This makes them an important topic for sustainable development. In this work, a model of stocks and flows for nonmetallic minerals in residential buildings, roads, and railways in the EU25, from 2004 to 2009 is presented. The changing material composition of the stock is modeled using a typology of 72 residential buildings, four road and two railway types, throughout the EU25. This allows for estimating the amounts of materials in in‐use stocks of residential buildings and transportation networks, as well as input and output flows. We compare the magnitude of material demands for expansion versus those for maintenance of existing stock. Then, recycling potentials are quantitatively explored by comparing the magnitude of estimated input, waste, and recycling flows from 2004 to 2009 and in a business‐as‐usual scenario for 2020. Thereby, we assess the potential impacts of the European Waste Framework Directive, which strives for a significant increase in recycling. We find that in the EU25, consisting of highly industrialized countries, a large share of material inputs are directed at maintaining existing stocks. Proper management of existing transportation networks and residential buildings is therefore crucial for the future size of flows of nonmetallic minerals.     

Insights on the Use of Hybrid Life Cycle Assessment for Environmental Footprinting

Establishing a comprehensive environmental footprint that indicates resource use and environmental release hotspots in both direct and indirect operations can help companies formulate impact reduction strategies as part of overall sustainability efforts. Life cycle assessment (LCA) is a useful approach for achieving these objectives. For most companies, financial data are more readily available than material and energy quantities, which suggests a hybrid LCA approach that emphasizes use of economic input‐output (EIO) LCA and process‐based energy and material flow models to frame and develop life cycle emission inventories resulting from company activities. We apply a hybrid LCA framework to an inland marine transportation company that transports bulk commodities within the United States. The analysis focuses on global warming potential, acidification, particulate matter emissions, eutrophication, ozone depletion, and water use. The results show that emissions of greenhouse gases, sulfur, and particulate matter are mainly from direct activities but that supply chain impacts are also significant, particularly in terms of water use. Hotspots were identified in the production, distribution, and use of fuel; the manufacturing, maintenance, and repair of boats and barges; food production; personnel air transport; and solid waste disposal. Results from the case study demonstrate that the aforementioned footprinting framework can provide a sufficiently reliable and comprehensive baseline for a company to formulate, measure, and monitor its efforts to reduce environmental impacts from internal and supply chain operations.     

Pulp Nonfiction: Regionalized Dynamic Model of the U.S. Pulp and Paper Industry

This article presents a modeling framework that enhances our ability to analyze the implications of policy for future sustainability of industrial systems. The framework quantifies the relationship between physical input and waste flows, capital vintage, and investment behavior in the U.S. pulp and paper industry. A regional vintage model is developed that simultaneously incorporates investment decisions, vintage structure of the capital stock, and physical material and energy flows, in addition to paper demand. Each capital vintage is specified by size, output structure, and age-specific retirement rates, as well as fiber use and energy intensities. Both embodied and disembodied technological change are incorporated, as well as greenhouse gas emissions from fuel use, and decomposition and incineration of waste. Estimated equations are used to simulate industrial futures until 2020, from a system of nonlinear differential equations.
Our results demonstrate the economic and physical inter-dependence between material and energy flows and the central role energy prices have in decision-making. For instance, an increase in average energy prices, ceteris paribus , will on average discourage paper recycling, which has implications for greenhouse gas emissions as well as for changes in energy intensity. The analysis of the data reveals diminishing rates of energy self-generation, and the immense longevity of capital, which hampers rapid change in input and carbon intensity. This stresses the importance of investment-led strategies in facilitating faster capital turnover to enhance future sustainability of the system.