Estimation of eco‐efficiency and identification of its influencing factors in China's Yangtze River Delta urban agglomerations

Zhang, Xinlin

doi:10.1111/grow.12376

Cited by 22 publications

(9 citation statements)

References 43 publications

(49 reference statements)

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“…To pursue maximum profits, local governments ignore environmental pollution in the process of energy consumption. The behaviour of pursuing economic growth at the expense of the environment is contrary to the purpose of fiscal decentralisation (Zhang, 2020). Mohamed et al (2020) also attested that a certain degree of fiscal decentralisation can negatively affect the energy sector.…”

Section: Spatial Regression Resultsmentioning

confidence: 99%

Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?

Wang

Hua

et al. 2020

Economic Research-Ekonomska Istraživanja

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This study applies the slack-based measure-data envelopment analysis (SBM-DEA) method to measure energy efficiency and tests the spatial spill-over effects of fiscal decentralisation, industrial structure and energy efficiency using the spatial Durbin model. The results show that China's energy efficiency presents a clear geographical step distribution, and the Eastern and Western regions have higher energy efficiency than the Central region. Fiscal decentralisation has a positive effect on the energy efficiency of the Eastern and Central regions by upgrading the industrial structure. In addition, fiscal decentralisation has significant, positive externalities on the surrounding areas, promoting environmental protection and energy conservation in all regions of China. The results are in line with the tenets of environmental federalism (Oates & Schwab): through the allocation and transfer of industrial factors, fiscal decentralisation affects energy governance. Therefore, local governments should formulate policies and targets according to their regions' different economic development levels, and the Central and Western areas, which have greater space for improvement and low energy efficiency, should receive attention to balance regional differences.

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Section: Spatial Regression Resultsmentioning

confidence: 99%

Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?

Wang

Hua

et al. 2020

Economic Research-Ekonomska Istraživanja

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“…In research aimed at evaluating the EE of cities, considering the common definition of eco-efficiency (ratio between economic value and environmental pressures), DEA-based models (linear programming analysis) have been presented to evaluate the EE of cities in China [11,53,54]. A city's eco-efficiency is in line with its level of economic development, with continuous cleaner production, circular economy, energy conservation moves and emissions reduction [53].…”

Section: Discussion Of Eco-efficiency Assessment Modelsmentioning

confidence: 99%

An AHP-Based Procedure for Model Selection for Eco-Efficiency Assessment

et al. 2021

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Modelling eco-efficiency is becoming a wide field of research shown by a large body of scientific literature on the subjects concerned. This paper, after performing a systematic literature review (SLR) to identify existing models for process eco-efficiency assessments, provides a methodology, based on the analytic hierarchy process (AHP) method, for choosing the eco-efficiency assessment model to be used for a given application (process, product, or service). For the SLR, papers from the databases Scopus, Web of Science and Science Direct were used. Forty articles were considered for this study, using as the main selection criterion articles that present an eco-efficiency assessment model, since the purpose was to survey the types of existing models that are used to assess processes, products or services for eco-efficiency. With the systematic review carried out, it was possible to identify the types of models that exist and how they are used in different sectors, always aiming to identify if what was analyzed is eco-efficient and what points need to be improved. The proposed AHP-based methodology was applied to a numerical model to outline how to apply the methodology. The approach was easy to use and effective in identifying the proper eco-efficiency model.

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“…3 In 2020, The annual average eco-efficiency of RED was 0.87 in the upper area, and it increased at the end of the study period. From 2006 to 2020, the eco-efficiency of RED in the upper area increased from 0.84 to 0.90, realizing the transformation from a medium level to a high level, possibly because most of these areas presented better eco-environmental quality and resource endowment, and tourism economy had a positive effect on eco-environmental protection.…”

Section: Figurementioning

confidence: 92%

“…In terms of the spatial-temporal evolutionary characteristics, scholars mainly analyze the change in eco-efficiency in terms of time and the distribution difference in space (Li et al, 2021). In terms of the influencing factors, scholars usually select the status of economic development, level of urbanization, and intensity of scientific and technological innovation as components of the ecoefficiency analysis system (Zhang, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial–Temporal pattern and evolutionary trend of eco-efficiency of real estate development in the yangtze river economic belt

Fan

Deng

et al. 2022

Front. Environ. Sci.

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The eco-efficiency of real estate development (RED) is an important indicator in evaluating the effectiveness of eco-civilization construction. Thus, analyzing its temporal evolution and spatial spillover effect can help to judge the degree of coordinated development between RED and eco-civilization construction in the Yangtze River Economic Belt (YREB). From an ecology-based angle of RED, the data of 108 cities in YREB from 2006 to 2020 were selected. Then, the Super-SBM model, Moran’s I model, and Markov chain model were used to measure the eco-efficiency value of RED and analyze its spatial–temporal evolutionary characteristics. Research results indicate that the eco-efficiency of RED in YREB increased by 7.3%. Differences were apparent in the regional eco-efficiency of RED, but the gap gradually narrowed, and the range decreased from 0.60 to 0.05. A positive spatial autocorrelation was observed in the eco-efficiency of RED, and the high–high (H-H) cluster areas showed a trend of expansion and transfer. The proportion of H-H cluster cities increased from 11 to 20%, whereas the low–low cluster areas showed a trend of small-scale diffusion. The eco-efficiency of RED exhibited consistently stable and “club convergence” characteristics. When the spatial spillover effect is ignored, the eco-efficiency of RED presents at least 55.1% probability to be maintained in the original state. By contrast, when the spatial spillover effect is considered, the probability can be increased, and the assimilation effect of transferring the eco-efficiency of RED can be enhanced. In the future, the overall eco-efficiency of RED in YREB can be improved by exploring new development technologies, establishing collaborative development mechanisms among cities, and adopting eco-protection-oriented reward and punishment policies.

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Estimation of eco‐efficiency and identification of its influencing factors in China's Yangtze River Delta urban agglomerations

Cited by 22 publications

References 43 publications

Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?

Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?

An AHP-Based Procedure for Model Selection for Eco-Efficiency Assessment

Spatial–Temporal pattern and evolutionary trend of eco-efficiency of real estate development in the yangtze river economic belt

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