Integrating Dynamic Material Flow Analysis and Computable General

Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Integrating Dynamic Material Flow Analysis and Computable General Equilibrium Models for Both Mass and Monetary Balances in Prospective Modeling: A Case for the Chinese Building Sector Zhi Cao,† Gang Liu,*,† Shuai Zhong,‡ Hancheng Dai,§ and Stefan Pauliuk∥

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†

SDU Life Cycle Engineering, Department of Chemical Engineering, Biotechnology, and Environmental Technology, University of Southern Denmark, 5230 Odense M, Denmark ‡ Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China § College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China ∥ Industrial Ecology Group, Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacher Strasse 4, D-79106 Freiburg, Germany S Supporting Information *

ABSTRACT: Integrated Assessment Models based on Computable General Equilibrium (IAM/CGE) and dynamic Material Flow Analysis (dynamic MFA) are two most widely used prospective model families to assess largescale and long-term socioeconomic metabolism (SEM) and inform sustainable SEM transition. The latter approach could complement the former by a more explicit understanding of service provision, in-use stocks, and material cycles in a mass balanced framework. In this paper, we demonstrated this by integrating the dynamic MFA and CGE model approaches for the Chinese building sector from 2012 to 2030. Our results revealed the impacts of building stock dynamics on sectoral and economywide CO2 emissions: lower service saturation levels and later saturation time of building stock development could free up investment on buildings and accumulatively save up to 25.4 Gt in embodied CO2 emissions of the building construction sector, representing a 2.7-fold of 2012 countrywide CO2 emissions. However, the save-ups are partly compensated by an increase of embodied CO2 emissions in the other sectors due to economy-wide rebound effect (ca. 18.8 Gt or about 74%). The integrated model we developed could help ensure both mass and monetary balances, explore rebound effects in prospective modeling, and thus better understand the economy-wide consequences of infrastructure development.

1. INTRODUCTION In 2015, the United Nations adopted a comprehensive 2030 Agenda on 17 Sustainable Development Goals (SDGs).1 A central challenge of achieving these 17 goals is to ensure human well-being improvement and address environmental sustainability (e.g., climate change) at the same time, which requires an understanding of the biophysical metabolism of materials and energies of modern human societies2 (termed below as socioeconomic metabolism, SEM). Urbanization and industrialization in developing countries will most likely lead to huge challenges on global climate change mitigation and resource depletion if developing countries are to achieve the same high level of in-use stocks in developed countries.3 How to improve human well-being while maintaining the SEM transformation in a sustainable direction, especially in lessdeveloped countries, is a critical knowledge gap that needs to be addressed4 via measuring and modeling the physically realistic climate change mitigation. To identify strategies toward a sustainable SEM transition and to estimate their system-wide impacts, researchers from different scientific communities have developed different © XXXX American Chemical Society

analytical approaches in the past decades (see Figure 1). Retrospective models give a snapshot or a sequence of snapshots of past and present SEM. On the basis of knowledge accumulated from retrospective studies, researchers from different communities also developed prospective models to explore scenarios of future SEM transition using different exogenous drivers. Two prospective model families are widely used to assess large-scale and long-term SEM changes: Integrated Assessment Models based on Computable General Equilibrium (IAM/ CGE) and dynamic Material Flow Analysis (dynamic MFA) model. The IAM/CGE model family quantitatively describes key processes in the human and earth systems, their interactions, and the market mechanism, aiming to provide scenarios for the future SEM.5 The CGE models, for example, determine household, investment, and government demands Received: Revised: Accepted: Published: A

July 2, 2018 December 3, 2018 December 4, 2018 December 4, 2018 DOI: 10.1021/acs.est.8b03633 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Figure 1. Summary and comparison of different SEM model families. Stationary material/substance flow analysis16 (stationary MFA/SFA), economic-wide material accounting17 (EW-MFA), attributional life-cycle assessment18 (ALCA), stationary environmentally extended input− output analysis (stationary EE-IO),19 dynamic material flow analysis20,21 (dynamic MFA), consequential life-cycle analysis18 (CLCA), and integrated assessment model/computable general equilibrium model5 (IAM/CGE).

for final products by respecting a utility maximization principle and market balance.6−10 However, they did not consider the in-use stocks of products and infrastructure (considered as capital goods11,12) and their dynamics (e.g., levels and development patterns).13 These product in-use stocks represent the physical basis for service provision (e.g., shelter and mobility) and production activities (e.g., energy and manufacturing plants), and thus their development over time (colloquially termed as “dynamics”) set boundary conditions for future resources demand, availability for recycling, and consequent resource, energy, and emission impacts.14 Considering these physical linkages and stock dynamics would allow for assessing the future pathways and path dependencies of SEM in a mass-balanced way.15 Dynamic MFA models take historic patterns and future dynamics of in-use stocks as cornerstones that connect human well-being to SEM by using the in-use stocks of a specific material (e.g., PVC,22 steel,23,24 copper,25 aluminum,26 cement,27 PBDE,28 and PCB29), bulk materials,3 or a specific product (especially buildings,30,31 and automobiles32,33) as the physical representation of human well-being. Several researchers attempted to further extend the dynamic MFA to explore life-cycle environmental impacts along with the development of in-use stocks14,34−39 using the level of in-use stocks as proxy for the housing, mobility, and production services in either static or dynamic models. However, a macroeconomic budget closure is missing in these dynamic MFA models, meaning that these models could not capture an economy-wide monetary balance. Without a monetary balance, the impact of final products flowing into one sector due to stock dynamics is not propagated to other economic sectors.40 Under a marketdriven (price) mechanism, the dynamics of in-use product

stocks not only determine embodied environmental impacts of in-use product stocks per se, but also alter the distribution of final products into other sectors and eventually lead to changes in economy-wide environmental impacts. For example, for a given GDP scenario, increasing investments in buildings caused by expansion of building stock will reduce the available fraction of GDP for other industrial sectors. Therefore, omitting the monetary balance in dynamic MFA models would fail to capture systematic responses (i.e., rebound effects41) to stock dynamics, i.e., reappropriations of final products, repercussions of stock dynamics on labor shifts and new capital allocation (i.e., technology deployment), and substitutions between labor, capital, and energy carriers. The CGE model families hold the market balance as central modeling principles, which could complement the dynamic MFA model family. There were few efforts10,42 on integrating these two model families; however, none of them presented the physical description of the stocks and the services they provide. An integrated approach taking advantage of the two model families could facilitate establishing the physical linkages between stock dynamics and economy-wide SEM, probing into the systematic responses to stock dynamics on the economywide SEM (e.g., CO2 emissions), assessing the CO2 emissions reduction potential of stock decoupling (e.g., lower stock saturation levels or slower stock growth), and eventually help identify physically realistic (mass balance consistent) mitigation pathways.4 Here, we developed a soft-linking technique to integrate the dynamic MFA model with the CGE model. We used Chinese building stock as a pilot case to demonstrate the opportunities of such an integration and the usefulness of the integrated model for assessing impacts of building stock (or human wellB

DOI: 10.1021/acs.est.8b03633 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Figure 2. Conceptual framework of integration of the CGE model with the dynamic MFA (DMFA) model. The components of the CGE model are shown in the upper left block, and the dynamic MFA model for the building stock is shown in the upper right block. P1−6 refer to the model integration procedures in Section 2.4. The CGE model is tuned so that its endogenously generated value-added curve of the building construction sector fits the value-added curve determined from the dynamic MFA model (soft linking).

2.2. Dynamic MFA Model. The dynamic MFA model for Chinese building stock consists of two parts: a historic estimation and a prospective simulation. We used a top-down stock estimation approach26,27 to retrospect the historic patterns of Chinese building stock from 1950 to 2015. On the basis of our previous work,43 we developed a stock-driven approach21 to determine future newly built floor areas from 2016 to 2030, based on observed historic patterns and assumed future development pathways of Chinese building stock. A four-parameter logistic and Gompertz combined function14,44 was used to simulate the growth curves of percapita building stock based on assumed saturation levels and times (see Figure 2). The stock-driven simulation took the same population projection data which were used in the dynamic recursive CGE model. Details of the dynamic MFA model for Chinese building stock are delineated in the Supporting Information (SI). 2.3. Dynamic Recursive CGE Model. We developed a recursive dynamic CGE model to depict Chinese economy based on our previous work.45 A CGE model describes the interactions between different agents (e.g., households, producers, and the government), representing flows of goods and services in a market-based economy. Labor and in-use stock for production (capital stocks) are two-factor inputs for the production sectors to transform natural resources into goods. The production sectors provide goods to final consumers. The CGE model also correspondingly represents a reverse flow of payments (money flow) to each flow of goods and services. The households receive payments by providing labor and in-use stock for production they own and then distribute the income to pay goods or services consumed. The rest of households’ income flows into savings or taxes to provide the funds for investment and government purchases. The investment compensates for depreciation in the current period and contributes to the accumulation of capital stock in the next period. In addition to capital accumulation, labor supply and productivity (or technical progress) are the other two determinants of the economic growth. To assess the impact of stock dynamics on energy use and CO2 emissions in different sectors, we used a five-tier nested constant elasticity of substitution (CES) function to present

being) development on CO2 emissions in a broader system that considers mass and monetary balance simultaneously. We explored the systematic responses to building stock’s dynamics on CO2 emissions, and specifically aimed at (i) exploring possible development pathways of Chinese building stock through 2030; (ii) estimating the impact of building stock dynamics on embodied CO2 emissions of China’s building construction sector; and (iii) unfolding the further perturbation of building stock dynamics on economy-wide CO2 emissions from the aggregate, production-based, and consumption-based perspectives during the period of 2012−2030.

2. METHODOLOGY 2.1. Conceptual Framework. We proposed a conceptual framework to position economic sectors in a CGE model from a stock-flow perspective (Figure 2). In this framework, the inuse stocks connect the natural environment and anthropogenic environment and thus provide services (e.g., mobility and shelter) for human well-being both directly and indirectly through converting resources from the natural environment into desirable final products (e.g., buildings, automobiles, and domestic appliances) that are the physical representation of services for human well-being. We divided final products into six broad categories, i.e., nondurable goods (e.g., food and water), durable goods (e.g., electronics and furniture), automobile, building, machinery, and infrastructure. The machinery stock and infrastructure stock together with labor input enable production activities. The other four categories directly provide service for human well-being. In addition to the service flows, we have also identified the physical flows that link environmental resources to final consumption of products in the conceptual framework (Figure 2). Monetary flows that reversely accompany the physical and service flows were used to depict the market transactions. We singled out the building stock to illustrate the integration of the dynamic MFA model and the CGE model. We translated impacts of building stock dynamics on building construction activities into monetary values based on the empirical regression between the value added and newly built floor areas and developed a soft-linking technique to link them to the CGE model (see details in Section 2.4). C


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Environmental Science & Technology Table 1. Saturation Levels and Saturation Times of Scenarios scenarios without-integration with-integration

reference saturation time/satuaration level low medium high

15 years low-15 medium-15 high-15



education, and recreation. A comparison of per capita floor areas between China and several developed countries is demonstrated in Figure S7. The saturation times (the time spans from 2012 to the time when stocks reach 98% of the saturation level) reflect a fast (15 years), medium (25 years), and slow (35 years) stock growth. In addition, we constructed a reference scenario (Reference) to represent the future SEM of Chinese economy without a model integration, which means, the dynamic recursive CGE model was ran alone. 2.5. Data Collection, Parameter Setting, CO2 Emissions Accounting, and Sensitivity Analysis. The historic built floor areas used in the top-down stock estimation were collected from the National Bureau of Statistics of China,46 where data are merely available from 1981 to 2015. We extrapolated the time-series of built floor areas during the period 1950−1980, based on their relationship with indices of value-added of construction. The indices of value-added of construction were collected from National Bureau of Statistics of China46 as well. The 46-sector Chinese SAM used in the dynamic recursive CGE model is derived from the 2012 Chinese input−output table released in 2015.47 Substitution elasticities used to calibrate the base year of the dynamic recursive CGE model and sector classification are presented in the SI. We used the method recommended by the IPCC to calculate CO2 emissions.48 CO2 emissions of each fuel type were estimated by multiplying the energy consumption with the respective emission factors of different fuel types and oxidization rates for different final use.49,50 CO2 emissions from cement production were taken from the latest previous estimate.50 The energy consumption data in 2012 were collected from Chinese Energy Statistical Yearbook.51 We employed consumption-based accounting approach to trace embodied CO2 emissions (see SI). The consumption-based accounting complements production-based accounting by allocating CO2 emissions occurring the production chain to the final demands.52 The consumption-based perspective sheds insight on the rebound effects of building stock dynamics on demands of investment and consumers and distribution of capital and labor. We assessed the robustness of models by conducting sensitivity analyses on the substitution elasticities. In order to perform the sensitivity analysis for eight groups of substitution elasticities (see Table S4 in the SI), we varied one group of substitution elasticities by ±10% while holding the others fixed. Detailed results of sensitivity analyses are presented in the SI.

the production process (see SI). The recursive dynamic CGE model is composed of a sequence of static equilibriums (i.e., one-year intervals). A 46-sector social accounting matrix (SAM) in the base year (2012) reflected the first equilibrium of the sequence (see Table S3 for a complete list of sectors). We calibrated the CGE model to the SAM in the base year (2012) and then ran the CGE model forward over time (2013−2030). The growth rate of total labor supply is exogenously determined by the population projection. The capital accumulation is endogenously determined by the investment and depreciation of capital. The growth rates of productivity are calibrated to observed historical numbers (2013−2015) and projected future trends (2016−2030) of economic growths. The details of projected population and economic growth are presented in the SI. 2.4. Models Integration and Scenarios. We singled out the building stock as an interface for soft-linking the dynamic MFA model to the recursive dynamic CGE model. We assumed that products flows into in-use product stocks of other sectors respect the distribution rules of products in the CGE model. The integration of models is implemented through the following six procedures (Figure 2): (i) We used the dynamic MFA model to simulate the newly built floor areas under various stock development pathways; (ii) We calculated value added of the building construction sector based on newly built floor areas and the empirical regression equation derived from value added and newly built floor areas (see Figure S3); (iii) We iteratively ran the recursive dynamic CGE model by changing the share parameter of investment demand for the building construction sector until the value added of this sector generated by the recursive dynamic CGE model is fairly close to that produced by the dynamic MFA model (soft linking; see details in Section S5 of the SI). (iv) The change in investment demand for the building construction sector is balanced out by demands of investment and consumers under the constraint of market balance. (v) The rebalancing of final demand leads to different sectoral outputs and changes in the distribution of capital and labor onto sectors under the constraint of given capital and labor supply. (vi) The substitution of capital, labor, and energies in different sectors are captured by the CES production functions chosen. We created saturation scenarios for the development of building stock, varying in assumed saturation level and time (see Table 1). A low (60 m2/capita), medium (70 m2/capita), and high (80 m/capita) saturation levels of building stock are set to represent various aspects of human well-being development in China, such as shelter, sanitation, healthcare,

3. RESULTS AND DISCUSSION 3.1. Impact of Stock Dynamics on Embodied CO2 Emissions of the Building Construction Sector. The simulation results demonstrate that the saturation level and time of building stock, to a large extent, determine future development pathways of newly built floor areas (Figure S5) and embodied CO2 emissions of the building construction sector (Figure 3a and 3b). During the period of 2012−2030 D


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Figure 3. Annual embodied CO2 emissions of building construction sector (a) and cumulative embodied CO2 emissions of building construction sector (b).

Figure 4. Sectoral breakdown of embodied CO2 emissions of the building construction sector under the Reference, High-15, and Low-35 scenarios for the model years 2012−2030 from a consumption-based perspective. Three biggest sectors: Manufacturing of Nonmetallic Mineral Products (S25), Smelting and Pressing of Ferrous Metals (S26), and Production and Supply of Electric Power and Heat Power (S39).

scenarios are 32.5−39.3 Gt (Low), 36.8−48.9 Gt (Medium), and 40.0−58.0 Gt (High), respectively. The difference of cumulative embodied CO2 emissions between two extreme scenarios (High-15 and Low-35) is 25.4 Gt. We singled out the Reference, High-15, and Low-35 scenarios, which respectively represent simulated results without model integration and two extreme stock development pathways with model integration, to illustrate the impact of stock dynamics on embodied CO2 emissions in more details. Sectoral breakdown of embodied CO2 emissions of the building construction sector under the chosen scenarios is graphically represented as heat maps in Figure 4 and results of other scenarios are demonstrated in Figure S8. During the period of 2012−2030, the trends of embodied CO2 emissions from each sector are, by and large, in line with the aggregate embodied CO2 emissions of the building construction sector. The most important sources of embodied CO2 emissions for

higher saturation levels result in higher embodied CO 2 emissions (Figure 3a) and meanwhile earlier saturation times lead to earlier emission peaks and higher cumulative CO2 emission of the building construction sector (Figure 3b). The 2030 embodied CO2 emissions of the building construction sector for low, medium and high building stock levels are approximately 1.2−1.3 Gt, 1.6−1.9 Gt, and 2.0−2.5 Gt, respectively. For the Reference scenario, annual embodied CO2 emissions of the building construction sector shows a steady growth, reaching at a level of 3.2 Gt by 2030. Under 15year saturation scenarios, the annual embodied CO2 emissions of the building construction sector reach a peak at 4.3 Gt (High-15) by 2025, 3.3 Gt (Medium-15) by 2024, and 2.4 Gt (Low-15) by 2021, respectively, representing an increase by a factor of 1.2−2.1 compared to 2012 levels (2.0 Gt). The 2012−2030 accumulated embodied CO2 emissions of the building construction sector under different saturation level E


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Figure 5. Annual economy-wide CO2 emissions (a) and cumulative economy-wide CO2 emissions (b).

Figure 6. Sectoral breakdown of economy-wide CO2 emissions under the Reference, High-15 and Low-35 scenarios from a production-based perspective. Five “gigaton” emittors: Manufacturing of Raw Chemical Materials and Chemical Products (S21), Manufacturing of Nonmetallic Mineral Products (S25), Smelting and Pressing of Ferrous Metals (S26), Production and Supply of Electric Power and Heat Power (S39), and Transport, Storage, and Post (S44).

3.2. Impact of Stock Dynamics on Economy-Wide CO2 Emissions. The variations of economy-wide CO2 emissions between scenarios reveal that the stock dynamics entail changes in economy-wide CO2 emissions (Figure 5). Higher saturation levels and earlier saturation times likewise lead to higher annual and cumulative CO2 emissions on an economy-wide basis. Annual CO2 emissions increase from 9.6 Gt in 2012 to 17.3 Gt in 2030 under the High-15 scenario (Figure 5a), while the annual CO2 emissions under the Low-35 scenario (i.e., lower saturation level and later saturation time) are 0.3−0.8 Gt lower during the period of 2016−2030. The 2012−2030 accumulated economy-wide CO2 emissions under High-15 scenario are 6.9 Gt higher than that under Low-35 scenario (Figure 5b). Figure 6 shows the sectoral breakdown of economy-wide CO2 emissions under the Reference, High-15, and Low-35 scenarios from a production-based perspective. Under the three scenarios, Manufacturing of Raw Chemical Materials and

the building construction sector involve three sectors: Manufacturing of Nonmetallic Mineral Products (S25), Smelting and Pressing of Ferrous Metals (S26), and Production and Supply of Electric Power and Heat Power (S39). Under the Reference, High-15, and Low-35 scenarios, the three sectors (S25, S26, and S39) altogether account for 76.78%, 76.76%, and 76.74%, respectively, of 2030 embodied CO2 emissions of the building construction sector. Embodied CO2 emissions from different sectors vary in scenarios. Under the Low-35 scenario, embodied CO2 emissions from the three biggest sectors (S25, S26, and S39) decrease to 0.24, 0.36, and 0.34 Gt, respectively, during the period of 2012−2030. Under the High-15 scenario, embodied CO2 emissions from the three biggest sectors (S25, S26, and S39) respectively increase from 0.34, 0.53, and 0.65 Gt in 2012, peak at 0.80, 1.25, and 1.26 Gt in 2025, and remain at the level of 0.45, 0.67, and 0.64 Gt in 2030. F


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Figure 7. Sectoral breakdown of economy-wide CO2 emissions under the High-15 (a) and Low-35 (b) scenarios from a consumption-based perspective and difference of cumulative CO2 emissions between the Low-35 and High-15 scenarios (c) for the time period 20122030. Six major balance-out sectors: S29−Manufacture of General Purpose Machinery, S30−Manufacture of Special Purpose Machinery, S31−Manufacture of Automobiles, S32−Manufacture of Railway, Ship, Aerospace and Other Transport Equipment, S33−Manufacture of Electrical Machinery and Apparatus, and S43−Civil Engineering Construction.

Chemical Products (S21), Manufacturing of Nonmetallic Mineral Products (S25), Smelting and Pressing of Ferrous Metals (S26), Production and Supply of Electric Power and Heat Power (S39) and Transport, Storage, and Post (S44) are the five “gigaton” contributors to economy-wide CO 2 emissions. The five sectors dominate the economy-wide CO2 emissions, together responsible for 81.4% (Reference scenario), 81.1% (High-15 scenario), or 80.6% (Low-35 scenario) of total emissions in 2030. The increasing trends of sectoral CO2 emissions over the period of 2012−2030 are consistent with that of economy-wide emissions. Under the Reference, High15, and Low-35 scenarios, CO2 emissions from the five “gigaton” contributors have risen up to about 1.43−1.44 Gt (S21), 0.99−1.29Gt (S25), 3.43−3.56 Gt (S26), 5.21−5.33 Gt (S39), and 1.26−1.27 Gt (S44) in 2030, respectively. As described in Figures 3 and 5, building stock development pathways have less significant impacts on economy-wide CO2 emissions compared with their impacts on embodied CO2 emissions of the building construction sector (S42). To illustrate the reasons behind, the sectoral breakdown of CO2 emissions under the High-15 and Low-35 scenarios from a consumption-based perspective are demonstrated in Figure 7. During the period of 2012−2030, the consumption-based CO2 emissions (i.e., embodied CO2 emissions) of the building construction sector in the Low-35 scenario are less than that in the High-15 scenario, totaling 25.4 Gt, and meanwhile the increase of consumption-based CO2 emissions in other sectors balance out the reduction in the building construction sector (Figure 7a and 7b), which cumulatively amounts to 18.8 Gt over the period of 2012−2030 (Figure 7c). Among the balance-out sectors, six sectors (i.e., S29−Manufacture of General Purpose Machinery, S30−Manufacture of Special Purpose Machinery, S31−Manufacture of Automobiles, S32−

Manufacture of Railway, Ship, Aerospace and Other Transport Equipment, S33−Manufacture of Electrical Machinery and Apparatus, and S43−Civil Engineering Construction) stand out as “gigaton” contributors that balance out the reduction of consumption-based CO2 emissions of the building construction sector, that is, 2.3 Gt (12.0%) for sector S29, 2.8 Gt (15.1%) for sector S30, 2.2 Gt (11.9%) for sector S31, 1.0 Gt (5.4%) for sector S32, 1.4 Gt (7.6%) for sector S33, and 5.6 Gt (29.9%) for sector S43, respectively (Figure 7c). The six “gigaton” contributors have a significantly high share parameter of investment demand (see details in Section S5 of the SI) and their supply chain is comparatively CO2-intenstive from the consumption-based perspective (Figure 7a and 7b). The balance-out effect in other sectors is relatively inconsiderable (e.g., 0.8 Gt (4.5%) in S46−Other Services, 0.8 Gt (4.1%) in S28−Manufacture of Metal Products, and 0.6 Gt (3.2%) in S34−Manufacture of Computers, Communication and Other Electronic Equipment). 3.3. Impact of Stock Development on Capital and Labor. Figure 8 shows the changes in distribution of capital and labor within sectors, as well as the consequential changes in sectoral emission intensities under two extreme stock development pathways scenarios (i.e., High-15 and Low-35). Lower saturation levels and later saturation times of the building stock require less capital accumulation and labor participation in the building construction sector. The surplus capital and labor would be allocated to other sectors, particularly in Manufacturing of General Purpose Machinery (S29), Manufacturing of Special Purpose Machinery (S30), Manufacturing of Automobiles (S31), Manufacturing of Railway, Ship, Aerospace and Other Transport Equipment (S32), Manufacture of Electrical Machinery and Apparatus (S33), and Civil Engineering Construction (S43). Under the G


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Figure 8. Trends of sectoral capitals (a), labors (b), and emission intensities (c) normalized by 2012 levels under the High-15 and Low-35 scenarios. Note: 1.5 means capital, labor, or emission intensity represents a 0.5-fold increase compared to its 2012 levels.

3.4. Discussion and Limitations. The results in Figures 5−8 demonstrate that positioning economic sectors in a CGE model from a stock-flow perspective and including the physical linkages and stock dynamics into the CGE model would enable both the mass balance and monetary balance simultaneously in prospective modeling of SEM transformation, particularly those modeling families with macroeconomic budget closure. The stock dynamics captured by the dynamic MFA model adds new constraints to the CGE model that is merely exogenously driven by population and affluence growth. The integrated models allow for assessing the effects of stock decoupling as new emissions mitigation options,40 via contrasting the results in the without-integration scenario to those in with-integration scenarios. A physical description of the stocks and the services they provide herein m2 of building area per capita, can help to create a meaningful future description of service levels instead of the rather abstract description of investment and stocks in monetary units. More

High-15 scenario, labors participated in these six sectors from 2012 to 2030 have scaled by a factor of 0.89 (S29), 0.92 (S30), 0.92 (S31), 1.00 (S32), 0.74 (S33), and 1.38 (S43), while capitals owned by these six sectors have scaled by a factor of 4.13 (S29), 4.07 (S30), 4.17 (S31), 4.27 (S32), 4.12 (S33), and 3.39 (S43), respectively. Under the Low-35 scenario, labors participated in these six sectors have scaled by a factor of 0.97 (S29), 1.04 (S30), 1.02 (S31), 1.12 (S32), 0.78 (S33) and 1.61 (S43), while capitals owned by these six sectors have scaled by a factor of 4.46 (S29), 4.55 (S30), 4.54 (S31), 4.67 (S32), 4.18 (S33), and 4.15 (S43), respectively. Compared to the High-15 scenario, increase in capitals and labors of these six sectors under the Low-35 scenario leads to substantial decline in their emission intensities. The differences of emission intensities of these sectors between the High-15 and Low-35 scenarios peak in 2025, which are in line with the trends of newly built floor areas. H



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importantly, the CGE model with comprehensive market modeling capacity could balance changes in supply and demand of product flows under different stock development scenarios. Stock decoupling in Chinese building sector could save tens of gigatons (around 25 Gt) CO2 emissions embodied in the building construction activities over the period of 2012− 2030, approximately representing a 2.7-fold of 2012 countrywide emissions; however, the economy-wide CO2 emissions savings would be reduced by almost three-quarters (around 19 Gt) if considering the repercussions of CO2 emissions in other sectors (especially S43−Civil Engineering Construction, which balances out ca. 30% of the economy-wide CO2 emissions savings), because savings in building construction sector are balanced out by rebound effects of embodied CO2 emissions in other sectors respecting the macroeconomic budget closure. In the context of China’s commitment on CO2 emissions reduction and the foreseeable infrastructure needs during the continuing urbanization, we believe such an integration of two models developed in this paper could help better understand the economy-wide consequences of infrastructure needs for human well-being improvement (e.g., various stock development scenarios), provide physically realistic strategy bundles for policy makers, and eventually explore the most effective mitigation pathways. Such a soft-linking technique used in the model integration of this case study have some limitations that should be addressed in the future. The first lies in the incomprehensive coverage of in-use product stock categories. The building stock is singled out to test the feasibility of integrating the CGE model and dynamic MFA model and to illustrate the impact of stock dynamics. To design more effective emissions mitigation strategies through stock decoupling, a full consideration of development pathways and patterns of all in-use product stocks is necessary in the next generations of prospective models. A flow matrix method12,53 could lay the pathways toward a finer resolution of in-use product stocks by disaggregating them into sectors. Another issue in our model is the lack of physical and monetary description of waste flows generated from the in-use product stocks and their potentials for recycling. Tracking the age profile of in-use stocks and introducing a waste market into the system could improve the representation of waste generation and treatment.54 An improved representation of waste flows could extend the spectrum of mitigation options (e.g., material-related strategies40,55), because stock dynamics could inform availability of recyclable postconsumer scraps and mitigation potentials of CO2 emissions via substitution of raw materials. Nevertheless, we believe that our attempt of model integration adds values to the prospective modeling community and can give some rough indication of the economy-wide impacts and the rebound effects of decoupling strategies in individual sectors.

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AUTHOR INFORMATION

Corresponding Author

*Phone: + 45 65509441; e-mail: [email protected] and [email protected]. ORCID

Zhi Cao: 0000-0001-6120-0362 Gang Liu: 0000-0002-7613-1985 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work is financially supported by the Danish Council for Independent Research (CityWeight; 6111-00555B), the Lighthouse ODEx funding of University of Southern Denmark (Building Passport; 95-443-64283), and National Natural Science Foundation of China (41728002). We highly appreciate the insightful comments from four anonymous reviewers.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b03633. Model details, additional figures, and data sources (PDF) I


Article

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Integrating Dynamic Material Flow Analysis and Computable General

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