Spatial-Temporal Heterogeneity for Commercial Building Carbon Emissions in China: Based the Dagum Gini Coefficient

Tian, Ma; Liu, Yi‐Sheng; Yang, Meng

doi:10.3390/su14095243

Cited by 17 publications

(11 citation statements)

References 31 publications

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“…(2) Dagum Gini coefficient and decomposition The Dagum Gini coefficient has been widely used to measure the spatial difference in economic and social phenomena [32][33][34]. This method divides the overall regional difference into three parts: intra-regional difference, inter-regional difference, and supervariable density.…”

Section: Regional Gap Analysis Methods For the Efficiency Of Marine G...mentioning

confidence: 99%

Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution

Wang,

Mao,

Zhu

et al. 2023

Fishes

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It is imperative to achieve the high-quality development of fisheries and green transformation of mariculture. Based on the data of marine aquaculture fisheries in China from 2006 to 2019, this research uses the Super-SBM (Slacks-Based Measure) model to measure the efficiency of marine green aquaculture in China and analyzes the regional gap, evolution trend, and driving factors of marine green aquaculture efficiency in nine coastal provinces in China. The results are as follows: (1) The Super-SBM measurement results showed that the efficiency of marine green aquaculture in China showed a fluctuating upward trend from 2006 to 2019. The results of nuclear density estimation and Dagum Gini coefficient show that there is an obvious regional gap in the efficiency of marine green aquaculture in China, and the regional gap is increasing with time. The Dagum Gini coefficient decomposition results show that inter-regional differences and supervariable density differences were the main sources of marine green aquaculture efficiency. The results of barycentric elliptic standard deviation show that the gravity center of marine green aquaculture efficiency in China shifted to the south from 2006 to 2019, indicating that the efficiency of marine green aquaculture in southern China has had a high degree of improvement. (2) The spatial Markov chain results show that spatial proximity plays a key role in the state transfer of marine green aquaculture efficiency. The higher the efficiency of marine green aquaculture in neighboring provinces, the greater the effect on improving the efficiency of marine green aquaculture in the province. The state transfer of marine green aquaculture efficiency usually occurs between similar horizontal states, and there is less “leapfrog” transfer. (3) The results of the geographic detector show that the number of professional mariculture practitioners is the core driving factor and plays a leading role in the regional gap in the efficiency of marine green aquaculture in China. The explanatory power of the interaction of the two factors on the regional gap of marine green aquaculture efficiency is more than 90%, which is much higher than the explanatory power of a single factor. The regional gap of marine green aquaculture efficiency is the result of multiple driving factors.

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Section: Regional Gap Analysis Methods For the Efficiency Of Marine G...mentioning

confidence: 99%

Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution

Wang,

Mao,

Zhu

et al. 2023

Fishes

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show abstract

“…The Gini coefficient as a widely employed measure around the world can assess the income inequality between citizens of a nation or region, and it is also often employed to assess the inequality of CE among nations (Cheng et al 2021, He et al 2022, Ma et al 2022). The study employed the Gini coefficient to evaluate the changes in regional disparities of CE within the YRD.…”

Section: Analyses Of the Spatial Patterns Of Estimated Carbon Emissionsmentioning

confidence: 99%

“…Regarding the spatiotemporal dynamics of CE, studied the spatiotemporal trends in energy consumption CE of China during 1995-2016. Ma et al(2022) analysed the spatiotemporal heterogeneity for commercial building CE in eight economic zones of China. Xu et al(2023a) analysed the features of county-level energy consumption CE in Northeast China during 2012-2020.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics of energy-related carbon emissions and their influencing factors in the Yangtze River Delta region of China based on nighttime light data

Xue,

Ma,

zhang

et al. 2024

Preprint

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Owing to China's massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions vary widely. Regional carbon emissions study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study's temporal coverage. On this basis, the distribution of energy-related carbon emissions (CE) in the Yangtze River Delta (YRD) region of China was estimated using the linear regression model at a multispatial scale. Then the spatiotemporal dynamics of CE were explored based on the estimated results. The four major factors' impact on CE in the YRD was assessed using the geographic detector (geodetector) model. The findings showed that the growth rate of CE in the YRD displayed three stages, and the total CE fluctuated upward. The spatial pattern of CE demonstrated a step-like decline from east to west. However, the Gini coefficient indicated that the differences in CE between cities gradually decreased since the CE had a strong spatial positive correlation in the YRD. Multiple factors affected the spatial variation of CE in the YRD, with economic level and population as the "critical" influencing elements, which determined the absolute amount of CE. The findings can provide scientific support for CE simulation in other similar regions.

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“…In this study, the Dagum Gini coefficient and the subgroup decomposition method were chosen to analyze spatial differences in the LUEE in urban agglomerations [68]. The Dagum Gini coefficient takes into account the crossover between samples and the distribution of sub-samples, allowing for more accurate tracking of the sources of spatial variation [69][70][71]. Before decomposing the Dagum Gini coefficient, each urban cluster should be ranked according to its average level of LUEE.…”

Section: The Dagum Gini Coefficientmentioning

confidence: 99%

Temporal–Spatial Variations and Convergence Analysis of Land Use Eco-Efficiency in the Urban Agglomerations of the Yellow River Basin in China

Kong

et al. 2023

Sustainability

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Achieving synergistic development of efficient urban land use and the natural environment is crucial in promoting green urbanization. The assessment of land use eco-efficiency (LUEE) and its temporal–spatial changes provides an effective means of quantifying the relationship between the urban ecological environment and land use. Targeting 55 selected cities in the Yellow River Basin (YRB), in this study, we utilize the Super-EBM method to gauge the LUEE. We explore the temporal patterns and the spatial convergence of LUEE utilizing kernel density estimation and spatial econometric methods. Considering the resource and environmental costs of land use, we assumed the industrial pollutant emissions generated during urban land use as the undesired outputs and designed a framework for measuring the level of LUEE under double constraints, which theoretically revealed the formation process and spatial convergence mechanism of LUEE. The results show the following: (1) Throughout the sample period, the LUEE of the YRB urban agglomeration decreased from 0.158 in 2009 to 0.094 in 2020, indicating a decreasing spatial disparity in LUEE over time. Notably, the Lanxi urban cluster exhibited the largest gap in LUEE, whereas the Guanzhong Plain urban agglomeration displayed the smallest gap. The hyper-variable density exceeded the inter-group gap as the main factor leading to the difference in LUEE. (2) Although the LUEE of urban agglomerations has increased, there still exists a noticeable polarization phenomenon. (3) The LUEE of YRB demonstrates a pattern of conditional convergence and exerts a significant spatial spillover effect. Over time, the LUEE of YRB will tend towards an individual steady state. The findings have implications for strengthening linkage and synergy among cities in YRB, promoting factor integration across administrative regions, and formulating heterogeneous policies.

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Spatial-Temporal Heterogeneity for Commercial Building Carbon Emissions in China: Based the Dagum Gini Coefficient

Cited by 17 publications

References 31 publications

Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution

Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution

Spatiotemporal dynamics of energy-related carbon emissions and their influencing factors in the Yangtze River Delta region of China based on nighttime light data

Temporal–Spatial Variations and Convergence Analysis of Land Use Eco-Efficiency in the Urban Agglomerations of the Yellow River Basin in China

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