Spatial–temporal characteristics and influencing factors of county-level carbon emissions in Zhejiang Province, China

Qi, Huibo; Shen, Xinyi; Long, Fei; Liu, Meijuan; Gao, Xiaowei

doi:10.1007/s11356-022-22790-7

Cited by 28 publications

(11 citation statements)

References 29 publications

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“…The DEA evaluation system takes the low-carbon management variables and effects as the input and output elements, respectively. In order to determine the county-level low-carbon management variables as input variables, this study refers to the typical variables of low-carbon cities, counties and influencing factors that affect carbon dioxide emissions published in international and Chinese journals [ 2 , 12 , 13 , 65 , 66 , 67 , 68 , 69 ]. On the basis of available data, we select the variables with significant correlation with carbon emission efficiency and remove the repetitive variables.…”

Section: Methodsmentioning

confidence: 99%

Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties

Zhang

Peng

Wang

et al. 2023

IJERPH

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Low-carbon management plays an important role in mitigating climate change and adapting to it. Localities should adopt differentiated low-carbon management policies according to the state of their environment. To help formulate specific and realistic low-carbon management policies, this paper took into account specific low-carbon management sectors. Likewise, it carefully considered the differences in various resource endowments and proposed a method for evaluating low-carbon management efficiency and potential. The method was applied to an empirical study from 2015 conducted on 1771 Chinese counties. Significant spatial heterogeneity was found during the research. The counties bordering central and Western China and the ones in the southeast coastal areas showed higher efficiency in the industrial sector. Southern and Northern China had higher efficiency in the housing and transportation sector, respectively. Moreover, counties in remote areas showed more potential in the industrial sector. Central China had higher potential in the housing sector, while counties bordering provinces had more potential in the transportation sector. Therefore, Chinese counties were divided into eight management zones where differentiated management strategies were identified to shape low-carbon management policies.

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Section: Methodsmentioning

confidence: 99%

Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties

Zhang

Peng

Wang

et al. 2023

IJERPH

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“…This indicated that these areas exhibited the robust stability in the local spatial structure of carbon emissions and showed a trend of spreading from southern and western Zhejiang to northern and eastern regions. The primary cause of this occurrence could be attributed to the fact that these regions predominantly comprise hilly counties with relatively low levels of economic development, substantial forest resources, and greater capacity for carbon storage [17].…”

Section: Analysis Of Relative Lengthmentioning

confidence: 99%

“…Temporally (Figure 7d), the suppressive effect weakened from 2002 to 2013, but gradually strengthened from 2013 to 2022. Qi's [17] study shows that the secondary industry occupies a high proportion of the total carbon emissions, but the tertiary industry has gradually become the main "contributor" to the increase in carbon emissions, with carbon emissions from the tertiary industry sectors such as transportation, logistics, and residential life sharply rising. Spatially (Figure 8d), the areas most negatively affected by the industrial structure were mainly concentrated in the eastern part of Shaoxing City, the northeastern part of Taizhou City, and the counties of Ningbo City.…”

Section: Construction Of Gtwr Model and Analysis Of Regression Model ...mentioning

confidence: 99%

Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China

Wang,

Yu,

et al. 2024

Land

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Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and neutrality. This study examines carbon emissions and economic and social problems data from 89 counties in Zhejiang Province. It employs analytical techniques such as LISA time path, spatio-temporal transition, and standard deviational ellipse to investigate the trends of carbon emissions from 2002 to 2022. Furthermore, it utilizes the GTWR model to evaluate the factors that influence these emissions on a county scale. The findings reveal the following: (1) The LISA time path analysis indicates a pronounced local spatial structure in the distribution of carbon emissions in Zhejiang Province from 2002 to 2022, characterized by increasing stability, notable path dependency, and some degree of spatial integration, albeit with a diminishing trend in overall integration. (2) The LISA spatio-temporal transition analysis indicates significant path dependency or lock-in effects in the county-level spatial clustering of carbon emissions. (3) Over the period 2002–2022, the centroid of carbon emissions in Zhejiang’s counties mainly oscillated between 120°55′15″ E and 120°57′01″ E and between 29°55′52″ N and 29°59′11″ N, with a general northeastward shift forming a “V” pattern. This shift resulted in a stable “northeast–southwest” spatial distribution. (4) Factors such as population size, urbanization rate, and economic development level predominantly accelerate carbon emissions, whereas industrial structure tends to curb them. It is crucial to customize carbon mitigation plans to suit the circumstances of each county. This study provides insight into the spatial and temporal patterns of carbon emissions at the county level in Zhejiang Province. It offers crucial guidance for developing targeted and practical strategies to reduce carbon emissions.

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“…If the values at nearby locations are similar, positive spatial autocorrelation occurs; if they are dissimilar, negative spatial autocorrelation occurs. The methods used in this study included the global Moran's index, local Moran's index, and Moran's scatter plot [26].…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Li,

Tian,

et al. 2023

Sustainability

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The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable region in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water resources, promoting biodiversity, and preserving cultural heritage. In this study, we employed machine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990–2010) and Landsat 8 (2015–2020) remote sensing data. The GeoDa software (software version number is 1.20.0.) platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinces such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When compared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRAs, explaining 41.6% of the variance. The random forest model also indicated positive effects for these factors, with high R2 values of 0.984 and 0.864 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB’s ecosystem and support sustainable development. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

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Spatial–temporal characteristics and influencing factors of county-level carbon emissions in Zhejiang Province, China

Cited by 28 publications

References 29 publications

Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties

Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties

Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

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