Jiaxing Pang

Dr. Jiaxing Pang

pangjx@lzu.edu.cn
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 April 2016 in Sustainability
Verified
Journal Article
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JPJiaxing Pang
Jiaxing Pang
XCXingpeng Chen
Xingpeng Chen
Zilong ZhangZilong Zhang
Zilong Zhang
HLHengji Li
Hengji Li
Eco-efficiency is a tool for sustainability analysis that indicates how to carry out economic activities effectively. This paper assesses agricultural eco-efficiency using data envelopment analysis (DEA) and the Theil index approach. Using basic data of 31 provinces in China during 2003–2013, we analyzed the agricultural eco-efficiency development level and spatial pattern in China. The results show that the agricultural eco-efficiency of only four provinces has been relatively efficient in the entire study period, namely, Zhejiang, Hainan, Chongqing, and Tibet. The results also show that agricultural eco-efficiency was higher mainly in south of the Qinling Mountains-Huaihe River Line and north of the Yangtze River area, that agricultural eco-efficiency is mainly affected by pure technical efficiency, and that highly efficient areas are mainly concentrated in the densely populated areas, i.e., the economic developed areas (except Tibet). The Theil index results show that the agricultural eco-efficiency difference weakened between provinces in China, as did western and northeast regions, but eastern and central regions show a slight upward trend.

JPJiaxing Pang
Jiaxing Pang
HLHengji Li
Hengji Li
Chengpeng LuChengpeng Lu
Chengpeng Lu
CLChenyu Lu
Chenyu Lu
XCXingpeng Chen
Xingpeng Chen
The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

15 December 2014 in Sustainability
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Journal Article
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XCXingpeng Chen
Xingpeng Chen
JPJiaxing Pang
Jiaxing Pang
Zilong ZhangZilong Zhang
Zilong Zhang
HLHengji Li
Hengji Li
As the largest solid waste (SW) generator in the world, China is facing serious pollution issues induced by increasing quantities of SW. The sustainability assessment of SW management is very important for designing relevant policy for further improving the overall efficiency of solid waste management (SWM). By focusing on industrial solid waste (ISW) and municipal solid waste (MSW), the paper investigated the sustainability performance of SWM by applying decoupling analysis, and further identified the main drivers of SW change in China by adopting Logarithmic Mean Divisia Index (LMDI) model. The results indicate that China has made a great achievement in SWM which was specifically expressed as the increase of ISW utilized amount and harmless disposal ratio of MSW, decrease of industrial solid waste discharged (ISWD), and absolute decoupling of ISWD from economic growth as well. However, China has a long way to go to achieve the goal of sustainable management of SW. The weak decoupling, even expansive negative decoupling of ISW generation and MSW disposal suggests that China needs timely technology innovation and rational institutional arrangement to reduce SW intensity from the source and promote classification and recycling. The factors of investment efficiency and technology are the main determinants of the decrease in SW, inversely, economic growth has increased SW discharge. The effects of investment intensity showed a volatile trend over time but eventually decreased SW discharged. Moreover, the factors of population and industrial structure slightly increased SW.

6 January 2021 in Sustainability
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Journal Article
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Chengpeng LuChengpeng Lu
Chengpeng Lu
XPXiaoli Pan
Xiaoli Pan
XCXingpeng Chen
Xingpeng Chen
JMJinhuang Mao
Jinhuang Mao
JPJiaxing Pang
Jiaxing Pang
BXBing Xue
Bing Xue
Waste is increasingly used as a renewable resource. Industrial symbiosis is an innovative concept for more efficient use of waste streams within industrial complexes, with the aim of reducing the overall environmental impact of the complex. Industrial symbiosis plays a more important role in promoting green economic growth and building low-carbon cities. Based on the ecological theoretical framework, combined with Waste Flow Analysis (WFA), the material flow analysis (MFA) and production matrix methods were used as the core to construct the Industrial Symbiosis System Waste Flow Metabolism Analysis (ISSWFMA) model. In addition, taking the “Jinchang Model” as an example, a typical case selected by the National Development and Reform Commission of China’s regional circular economy development model, we conducted a refined quantitative study on the flow and metabolism of waste flow in the regional industrial symbiosis system at the City-Region level using the circulation degree index. The following conclusions were obtained from the study: The ISSWFMA model can better describe the flow and metabolism of waste streams in the industrial symbiosis system at the City-Region Level and can provide data and methods for storage management. As the internal industrial chain and the correlation between various departments continuously improved, the Circulation Index (CI) of solid waste, wastewater, and exhaust gas in the industrial symbiosis system of Jinchang City showed an overall increasing trend, the degree of recycling was continuously increasing, the industrial symbiosis ability was continuously enhanced, and the system structure was more complete. At the same time, based on the analysis of different wastes, the industrial symbiosis is developed at different stages; based on the analysis of solid wastes, the industrial symbiosis ability of Jinchang’s Industrial Symbiosis System has strengthened and accelerated the fastest from 2005 to 2010; based on the analysis of wastewater, the industrial symbiosis ability of the system strengthened slowly during the whole study period; and based on the analysis of exhaust gas, the industrial symbiosis ability of the system continued to strengthen rapidly during the whole study period. Finally, on the basis of further discussion on the selection of waste recycling paths, we proposed to give full play to the role of market mechanisms, and to build recycling areas and ecological areas by strengthening industrial symbiosis and its derived urban symbiosis to achieve the goals of natural resource conservation, ecological environment protection, and harmonious coexistence between human and nature.

29 April 2022 in Environmental Science and Pollution Research
Verified
Journal Article
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ZLZhi Long
Zhi Long
JPJiaxing Pang
Jiaxing Pang
SLShuaike Li
Shuaike Li
JZJingyi Zhao
Jingyi Zhao
TYTing Yang
Ting Yang
BWBo Wang
Bo Wang