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Cheng S, Fan W, Meng F, Chen J, Liang S, Song M, Liu G, Casazza M. Potential Role of Fiscal Decentralization on Interprovincial Differences in CO 2 Emissions in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:813-822. [PMID: 33373195 DOI: 10.1021/acs.est.0c04026] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Spatial differences in CO2 emissions must be taken into account in CO2 mitigation. In this work, a spatial within-between logarithmic mean Divisia index decomposition model was developed by using cluster analysis to evaluate the potential role of fiscal decentralization in driving interprovincial differences in CO2 emissions in China. The results revealed that the direct impact of fiscal decentralization emerged as a major emission driver after 2009. The differences of provincial CO2 emissions from the national average can be mainly attributed to emission differences between the distinct provincial clusters. The direct and indirect impacts of fiscal decentralization contributed to the shaping of differences in CO2 emission between provinces and their provincial cluster average, and between provincial cluster average and the national average. Reducing the differences in CO2 emission between distinct provincial clusters should be considered a breakthrough for the Chinese government. The provinces with CO2 emissions below the national average and above the average emissions of its provincial cluster still have the potential for further mitigation. Optimizing the expenditure authority of the central and provincial governments and improving the energy efficiency of the provincial fiscal expenditure are the two effective ways to further promote CO2 mitigation.
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Affiliation(s)
- Shulei Cheng
- School of Public Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China
| | - Wei Fan
- West Center for Economic Research, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China
| | - Fanxin Meng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Jiandong Chen
- School of Public Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China
| | - Sai Liang
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Malin Song
- School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China
| | - Gengyuan Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Marco Casazza
- Department of Sciences and Technologies, University of Naples "Parthenope", Centro Direzionale, Isola C4, 80143 Naples, Italy
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Li Y, Zhang Y, Yu X. Urban weight and its driving forces: A case study of Beijing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 658:590-601. [PMID: 30580214 DOI: 10.1016/j.scitotenv.2018.12.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/03/2018] [Accepted: 12/03/2018] [Indexed: 05/05/2023]
Abstract
Massive resource and energy consumption greatly increase a city's domestic material consumption (its "weight"), resulting in proportionally large pressure on the environment. Understanding the causes and driving forces behind urban weight can therefore promote more sustainable urban development. To improve our understanding, we analyzed the composition of Beijing's urban weight from 2000 to 2015. We then defined the key metabolic processes (including both metabolic components and paths between them) that contributed to weight changes during this period using the logarithmic mean Divisia index model to identify the underlying driving forces. Beijing's weight grew by 82.6% during the study period, with non-metallic minerals being the material most consumed (about 45% of the total weight). Beijing depended heavily on imports, and extraction within the city's administrative boundaries has gradually declined. The Construction and Industry sectors were the dominant metabolic components; their material exchanges with both the internal and external environments of the city represented the largest flow pathways. The largest proportion of urban weight that was dissipated was air pollution, which therefore represents the biggest environmental challenge facing Beijing. Economic activity was the largest driver of urban weight growth, followed by population growth, with decreasing material consumption intensity (per unit GDP) somewhat reducing the city's weight. The results help us to understand the characteristics of urban weight change over a long period, and this knowledge can be used to prioritize structural adjustment, determine targets for regulation, refine processes, and guide future policy development to focus on the drivers of weight change.
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Affiliation(s)
- Yanxian Li
- State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China; School of Environment, Beijing Normal University, Beijing 100875, China
| | - Yan Zhang
- State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China; School of Environment, Beijing Normal University, Beijing 100875, China.
| | - Xiangyi Yu
- Solid waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Yuhuinanlu No. 1, Chaoyang District, Beijing, 100029, China.
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Psarras PC, Comello S, Bains P, Charoensawadpong P, Reichelstein S, Wilcox J. Carbon Capture and Utilization in the Industrial Sector. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2017; 51:11440-11449. [PMID: 28858476 DOI: 10.1021/acs.est.7b01723] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The fabrication and manufacturing processes of industrial commodities such as iron, glass, and cement are carbon-intensive, accounting for 23% of global CO2 emissions. As a climate mitigation strategy, CO2 capture from flue gases of industrial processes-much like that of the power sector-has not experienced wide adoption given its high associated costs. However, some industrial processes with relatively high CO2 flue concentration may be viable candidates to cost-competitively supply CO2 for utilization purposes (e.g., polymer manufacturing, etc.). This work develops a methodology that determines the levelized cost ($/tCO2) of separating, compressing, and transporting carbon dioxide. A top-down model determines the cost of separating and compressing CO2 across 18 industrial processes. Further, the study calculates the cost of transporting CO2 via pipeline and tanker truck to appropriately paired sinks using a bottom-up cost model and geo-referencing approach. The results show that truck transportation is generally the low-cost alternative given the relatively small volumes (ca. 100 kt CO2/a). We apply our methodology to a regional case study in Pennsylvania, which shows steel and cement manufacturing paired to suitable sinks as having the lowest levelized cost of capture, compression, and transportation.
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Affiliation(s)
- Peter C Psarras
- Department of Chemical and Biological Engineering, Colorado School of Mines , Golden, Colorado 80401, United States
| | - Stephen Comello
- Graduate School of Business, Stanford University , Stanford, California 94305, United States
| | - Praveen Bains
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
| | - Panunya Charoensawadpong
- Department of Energy Resources Engineering, Stanford University , Stanford, California 94305, United States
| | - Stefan Reichelstein
- Graduate School of Business, Stanford University , Stanford, California 94305, United States
| | - Jennifer Wilcox
- Department of Chemical and Biological Engineering, Colorado School of Mines , Golden, Colorado 80401, United States
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Hu Y, Lin J, Cui S, Khanna NZ. Measuring Urban Carbon Footprint from Carbon Flows in the Global Supply Chain. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:6154-6163. [PMID: 27232444 DOI: 10.1021/acs.est.6b00985] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A global multiregional input-output (MRIO) model was built for eight Chinese cities to track their carbon flows. For in-depth understanding of urban carbon footprint from the perspectives of production, consumption, and trade balance, four kinds of footprints and four redefined measurement indicators were calculated. From the global supply chain, urban carbon inflows from Mainland China were larger than outflows, while the carbon outflows to European, principal North American countries and East Asia were much larger than inflows. With the rapid urbanization of China, Construction was the largest consumer and Utilities was the largest producer. Cities with higher consumption (such as Dalian, Tianjin, Shanghai, and Beijing) should change their consumption patterns, while cities with lower production efficiency (such as Dalian, Shanghai, Ningbo, and Chongqing) should improve their technology. The cities of net carbon consumption tended to transfer carbon emissions out of them by trading in carbon-intensive products, while the cities of net carbon production tended to produce carbon-intensive products for nonlocal consumers. Our results indicated that urban carbon abatement requires not only rational consumption and industrial symbiosis at the city level, but also tighter collaboration along all stages of the global supply chain.
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Affiliation(s)
- Yuanchao Hu
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences , Xiamen 361021, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Jianyi Lin
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences , Xiamen 361021, China
- Energy Analysis and Environmental Impacts Department, Energy Technologies Area, Lawrence Berkeley National Laboratory , 1 Cyclotron Road, MS 90R2002, Berkeley, California 94720, United States
| | - Shenghui Cui
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences , Xiamen 361021, China
| | - Nina Zheng Khanna
- Energy Analysis and Environmental Impacts Department, Energy Technologies Area, Lawrence Berkeley National Laboratory , 1 Cyclotron Road, MS 90R2002, Berkeley, California 94720, United States
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