Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Sun, Zuoren; An, Chao; Sun, Huachen

doi:10.20944/preprints201806.0080.v1

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…Spatial autocorrelation is usually used to analyze the correlation level between the attribute eigenvalue of a unit in a certain region of space and the same attribute eigenvalue of the unit in its adjacent region, which can be divided into global and local spatial autocorrelation [39,40]. Global spatial autocorrelation mainly analyzes whether regions have spatial relevance on the whole level.…”

Section: Spatial Autocorrelation Analysis Methodsmentioning

confidence: 99%

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Shi

Hao

et al. 2022

Water

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The scientific evaluation of water pollution in the Yellow River Basin was directly related to the sustainable utilization of water resources and the green development of the agricultural economy in this region. In this study, we focused on the planting industry, and measured the agricultural grey water footprint of 73 prefecture-level cities in the Yellow River Basin from 2000 to 2019. We used spatial autocorrelation analysis to reveal temporal and spatial differentiation characteristics, and we used the path analysis method to study the factors influencing the temporal evolution and spatial distribution. Taking 2015 as the study period, the agricultural grey water footprint showed a trend of first rising and then falling. The values and growth rates of the agricultural grey water footprint in different regions were quite different. According to the natural breakpoint method, the agricultural grey water footprints were divided into low, middle, high, and very high groups. There were obvious spatial differences in the agricultural grey water footprints, and these differences gradually decreased. Generally, the H–L and the L–L types were dominant. From 2000 to 2019, most prefecture-level cities maintained the same transition changes as those in the neighboring regions. Crop yield, economic scale, population scale, urban and rural structure, and technological innovation were found to be the key elements of spatiotemporal variation in the agricultural grey water footprint.

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Section: Spatial Autocorrelation Analysis Methodsmentioning

confidence: 99%

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Shi

Hao

et al. 2022

Water

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Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

et al. 2020

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Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic reasoning approach can also be used as cognitive scaffolding to educe AI-Thinking in the analysts to ask more questions and provide decision making support to inform policy making in sustainable development. This paper uses the 2018 Environmental Performance Index (EPI) data from 180 countries which includes performance indicators covering environmental health and ecosystem vitality. AI-based predictive modeling techniques are applied on 2018 EPI data to reveal the hidden tensions between the two fundamental dimensions of sustainable development: (1) environmental health; which improves with economic growth and increasing affluence; and (2) ecosystem vitality, which worsens due to industrialization and urbanization.

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A New Study on Air Quality Standards: Air Quality Measurement and Evaluation for Jiangsu Province Based on Six Major Air Pollutants

Liu

Gao

2018

Sustainability

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China’s current Air Quality Index (AQI) system only considers one air pollutant which has the highest concentration value. In order to comprehensively evaluate the urban air quality of Jiangsu Province, this paper has studied the air quality of 13 cities in that province from April 2015 to March 2018 based on an expanded AQI system that includes six major air pollutants. After expanding the existing air quality evaluation standards of China, this paper has calculated the air quality evaluation scores of cities in Jiangsu Province based on the six major air pollutants by using the improved Fuzzy Comprehensive Evaluation Model. This paper has further analyzed the effectiveness of air pollution control policies in Jiangsu Province and its different cities during the study period. The findings are as follows: there are distinct differences in air quality for different cities in Jiangsu Province; except for coastal cities such as Nantong, Yancheng and Lianyungang, the southern cities of Jiangsu generally have better air quality than the northern cities. The causes of these differences include not only natural factors such as geographical location and wind direction, but also economic factors and energy structure. In addition, air pollution control policies have achieved significant results in Nantong, Changzhou, Wuxi, Yangzhou, Suzhou, Yancheng, Zhenjiang, Tai’an and Lianyungang. Among them, Nantong has seen the biggest improvement, 20.28%; Changzhou and Wuxi have improved their air quality by more than 10%, while Yangzhou, Suzhou, and Yancheng have improved their air quality by more than 5%. However, the air quality of Nanjing, Huai’an, Xuzhou, and Suqian has worsened by different degrees compared that of the last period within the beginning period, during which Suqian’s air quality has declined by 20.07% and Xuzhou’s by 16.32%.

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Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Cited by 5 publications

References 4 publications

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

A New Study on Air Quality Standards: Air Quality Measurement and Evaluation for Jiangsu Province Based on Six Major Air Pollutants

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