Reallocation planning of urban industrial land for structure optimization and emission reduction: A practical analysis of urban agglomeration in China’s Yangtze River Delta

Hui, S.Y.R.; Xiong, Pingping

doi:10.1016/j.landusepol.2018.11.034

Cited by 68 publications

(24 citation statements)

References 32 publications

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“…The mean absolute deviations in the Middle Yangtze Urban Agglomeration fell between 11.1% and 13.8%, and an analysis of influencing factors in the region revealed that the impact of PSI is declining, while the impact of PCCEUR is increasing (Section 3.2). This is consistent with cities in the region undergoing industrial structural transformation and population agglomeration [ 77 ]. Cities in the Chengdu-Chongqing Urban Agglomeration (except for Chongqing) exhibited mean absolute deviations between 12.5% and 21.9% that were the largest mean absolute deviation values among the three urban agglomerations evaluated here.…”

Section: Resultssupporting

confidence: 85%

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

2021

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China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

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Section: Resultssupporting

confidence: 85%

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

2021

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“…There are abundant research results on the adjustment and optimization of land use layout, which have gone through a research process from qualitative analysis to quantitative calculation, and quantitative structural configuration to spatial distribution. Most of the research is based on the consideration of land quality, ecology, function, food production, and economic benefit to optimize the land use layout [17][18][19][20][21], which can be divided into two aspects: Quantity optimization and space optimization of land use.…”

Section: Introductionmentioning

confidence: 99%

Cultivated Land Use Layout Adjustment Based on Crop Planting Suitability: A Case Study of Typical Counties in Northeast China

Zhang

2021

Land

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Cultivated land use layout adjustment (CLULA) based on crop planting suitability is the refinement and deepening of land use transformation, which is of great significance for optimizing the allocation of cultivated land resources and ensuring food security. At present, people rarely consider the land suitability of crops when using cultivated land, resulting in an imbalance between crop distribution and resource conditions such as water, heat, and soil, and adversely affects the ecological security and utilization efficiency of cultivated land. To alleviate China’s grain planting structural imbalance and efficiency loss, this paper based on the planting suitability of main food crops (rice, soybean, and maize) to adjust and optimize the cultivated land use layout (CLUL) in the typical counties of the main grain production area in Northeast China, using the agent-based model for optimal land allocation (AgentLA) and GIS technology. Findings from the study show that: (1) The planting suitability of rice, soybean, and maize in the region is obviously different. Among them, the suitability level of soybean and maize is high, and that of rice is low. The current CLUL of the food crops needs to be further optimized and adjusted. (2) By optimizing the layout of rice, soybean, and maize, the planting suitability level of the food crops and the concentration level of the CLUL spatial pattern have been improved. (3) The plan for CLULA is formulated: The study area is divided into rice stable production area, maize-soybean rotation area, maize dominant area, and soybean dominant area, and town or village is identified as the implementation unit of CLULA. The plan for CLULA will be conducive to the concentrated farming of food crops according to the suitable natural conditions and management level. The research realized the optimization of spatial structure and cultivated land use patterns of different food crops integrating farming with protecting land. The significance of the study is that it provides a scientific basis and guidance for adjusting the regional planting structure and solving the problem of food structural imbalance.

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“…'Environment Selfpurification Pressure' is influenced by drainage of the industrial wastewater (E3), emissions of SO 2 (E4) (considering the main exhaust gas in Shanxi is SO 2 ) and industrial solid waste (E5) [34]- [36]. 'Economic Pressure' might target the economic development demand for natural resources, which can be represented by Per Capita GDP (E6) and fixed investments (E7) [37], [38].…”

Section: A Ecosystem Interaction Indicatorsmentioning

confidence: 99%

Environmental Risk and Policy Choices in an Energy Intensive Region of China—An Empirical Study in Shanxi Province

et al. 2020

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Given that there has been ecological degradation for a long time in Shanxi Province, China. This research addresses this situation by analyzing the energy structure and environmental risk in the past and in the future. Consequently, this study uses two indicator systems, ecosystem interaction system and environmental risk representation, to illustrate the environmental risk in an energy intensive region of China. And we build a BP-SVM model applying a back-propagation (BP) neural network and support vector machine (SVM) arithmetic to predict the future ecology-economy-society system interactions in Shanxi Province. At last, we classify the risk rank by using environmental risk representation indicators. The main conclusions from this research are as follows: Firstly, two indicator systems have advantages in describing the ecological-economy-society interaction especially the human society's impact on the ecosystem over a single indicator system. Secondly, by the BP-SVM model, there is a relatively high ecological risk rank in next few years in Shanxi Province, although it fluctuates occasionally. Finally, this study not only offers recommendations for the government to develop policies to transform from a coal-energy based system to a clean, safe, and efficient modern energy system, but also points out the implications for government administrations in energy intensive areas of developing countries to guide the economic transformation. INDEX TERMS Energy intensive region, environmental risk, ecosystem interaction prediction, risk recognition, policy implications.

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Reallocation planning of urban industrial land for structure optimization and emission reduction: A practical analysis of urban agglomeration in China’s Yangtze River Delta

Cited by 68 publications

References 32 publications

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

Cultivated Land Use Layout Adjustment Based on Crop Planting Suitability: A Case Study of Typical Counties in Northeast China

Environmental Risk and Policy Choices in an Energy Intensive Region of China—An Empirical Study in Shanxi Province

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