Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China

Luo, Hui; Li, Yingyue; Gao, Xinyu; Meng, Xiangzhao; Yang, Xiaohu; Yan, Jinyue

doi:10.1016/j.apenergy.2023.121488

Cited by 26 publications

(5 citation statements)

References 82 publications

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“…In addition, land use change is a random process with no consequences [44]. The Markov transfer matrix was used to determine the target year's land-use scenarios, and MC was used to predict the changes in land-use [45]. The formula is as follows:…”

Section: Markov Chainmentioning

confidence: 99%

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

Che,

Yin,

Jin

et al. 2024

Land

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The urban ecological environment is crucial to the quality of life of residents and the sustainable development of the region, and the assessment and prediction of the ecological environment quality can provide a scientific guidance for ecological environment management and improvement. We proposed a novel approach to assess and simulate the urban ecological environment quality using the Geographic Information System Ecological Index (GISEI). First, we calculated the remote sensing ecological index (RSEI) for Xi’an in 2020. Second, we selected land use data, mean annual temperature, and mean annual relative humidity as ecological indicators. We regressed these indicators on the RSEI to obtain the GISEI of Xi’an in 2020. Finally, we simulated the GISEI of Xi’an in 2030 by predicting the ecological indicators and analyzed the changes in the ecological environment quality. The results of the study show that the ecological environment quality in Xi’an in 2020 is better overall. By 2030, most of the ecological environment quality in Xi’an will be worse, and the proportion of the excellent area will decrease from 42.8% to 3.8%. The more serious ecological degradation is mainly located in the regions bordering the Qinling Mountains and the Guanzhong Plain, and the ecological environment quality in most areas of the Qinling Mountains will deteriorate from excellent to good.

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Section: Markov Chainmentioning

confidence: 99%

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

Che,

Yin,

Jin

et al. 2024

Land

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“…Nonetheless, prevailing research perspectives largely hinge on static assessments of the current land use scenario within the study area. Most research has centered around larger scales such as the national level 17 – 19 , provinces and cities 20 – 22 , and watersheds 17 , 23 . There exists a notable dearth of research at the county level, particularly in the examination of carbon revenue and expenditure trends predicated on prognostications of forthcoming land use alterations within counties.…”

Section: Introductionmentioning

confidence: 99%

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China

Wei,

Yang,

Wang

et al. 2024

Sci Rep

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The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.

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“…They employed the ridge regression model and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to explore the influence of key factors on carbon emissions in the Chengdu-Chongqing urban agglomeration. Luo et al [26] used data from Xi'an as an example to establish a spatial simulation and prediction model of carbon emissions, with the aim of providing references for the regional planning of carbon emission reduction and the implementation of carbon emission reduction technologies. Some scholars [27] chose to start from the land use to assess the impact of land use patterns on carbon emissions under the Yellow River Delta region, providing a theoretical framework for sustainable land use.…”

Section: Introductionmentioning

confidence: 99%

Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China

Sun,

Liang,

Meng

et al. 2024

Land

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Industrialization has increased global carbon emissions, necessitating effective climate change mitigation measures. China, the most populous developing nation, faces the challenge of strategizing emissions to meet national carbon neutrality objectives. However, research on specific regions’ carbon emissions drivers and causal factors is limited, particularly across prefectural-level cities. This study estimates the spatial and temporal patterns of carbon emissions across China’s prefectural cities and utilizes both OLS regression and stepwise regression models to analyze the impact of various factors influencing carbon emissions in these cities. Results reveal the following: (1) The country’s overall 20-year carbon emissions continue to grow from 3020.29 Mt in 2001 to 9169.74 Mt in 2020, with an average annual growth rate of 5.71%; the eastern region has seen a gradual deceleration in emissions, whereas the western region continues to experience an increase. Carbon emissions in cities within each subregion consistently rise. (2) Carbon emissions in Chinese prefectural-level cities exhibit strong spatial autocorrelation and clustering (Z > 1.96, p < 0.05), with hot spots primarily in the eastern coastal areas and cold spots in the northwest to southwest regions. (3) Economic and demographic factors significantly increase carbon emissions, while climate and urbanization effects are more complex and variable. Economic growth and population increase are the most significant influencing factors, but regional variances exist in carbon emissions determinants in subregional prefectural cities. These insights provide valuable insights into national emission dynamics at the prefectural level, providing a theoretical basis for enhancing carbon emission strategies across various jurisdictions.

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Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China

Cited by 26 publications

References 82 publications

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China

Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China

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