Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Liu, Kai; Ni, Ziyi; Ren, Mei; Xiao-qing, Zhang

doi:10.3390/ijerph19116427

Cited by 18 publications

(12 citation statements)

References 65 publications

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Section: A Carbon Neutralization In Citymentioning

confidence: 99%

“…As mentioned above, cities contribute 85% of global industrial direct carbon emissions [2]. Due to continuous urbanization [2], cities have become key places for carbon neutrality in many countries [29]- [31]. Carbon mitigation and carbon sink increment are the two paths most often taken toward achieving urban carbon neutrality.…”

Section: A Carbon Neutralization In Citymentioning

confidence: 99%

“…Timing embedding. Four seasons were chosen as the basis for time coding so that CiSL-NPP would imply global climate change information.For each image, we assign a number according to the season in which it was taken, that is the spring, summer, autumn and winter correspond to 0, 1, 2 and 3 respectively.The numbers Patch (i.e., session) were projected to the same size as the position Embedding in MAE according to (2):…”

Section: Data Generationmentioning

confidence: 99%

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A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes. INDEX TERMSNeural network Learning System, Unsupervised generation model, Spatio-temporal Masked AutoEncoder, Carbon Storage Capacity Measurement I. INTRODUCTION M ORE than 90% of human economic activity takes place in cities [1], which directly contribute at least 85% of global carbon emissions [2] and are regarded as the world's main battlefields of carbon neutrality [3]. Currently, carbon mitigation and carbon sink increment are the primary approaches to achieving urban carbon neutrality. The carbon mitigation approach, a complex systematic undertaking, is difficult to implement [4]. Further, strict policies towards emissions may have consequences such as carbon leakage [5], [6]. The carbon sink increment approach shows more potential to achieve urban carbon neutrality. For example, studies indicate that the carbon sink of urban vegetation can offset more than 22.45% of carbon emissions [7], already achieving carbon neutrality in some areas [8]. In city, limited to a fixed-size space, the effective way to increase carbon sink is to use green resources (e.g., municipal parks) rationally according to their vegetation carbon storage capacity. Net Primary Productivity (NPP), a crucial metric reflecting the storage effect of vegetation , is the key role for increasing carbon storage capacity in the city ...

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Section: A Carbon Neutralization In Citymentioning

confidence: 99%

Section: A Carbon Neutralization In Citymentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

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A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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“…In order to apply policies and technologies to reduce greenhouse gas emissions, it is essential to identify the characteristics of each city first. This is because each city has unique physical and non-physical characteristics, such as size, function, spatial structure, and industrial composition, which affect the type of emissions or absorption [4][5][6][7][8][9][10][11]. Solutions for carbon reduction should be applied by comprehensively considering the social, economic, and environmental characteristics of each city.…”

Section: Introductionmentioning

confidence: 99%

Towards Carbon-Neutral Cities: Urban Classification Based on Physical Environment and Carbon Emission Characteristics

Lee

Jung

2023

Land

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Cities are highly industrialized and populated areas and major sources of greenhouse gas emissions. For carbon neutrality, examining the correlation between urban characteristics and greenhouse gas emissions is necessary. This study aimed to analyze the characteristics of each city from a carbon neutrality perspective. As such, we conducted a carbon-neutral city analysis. First, the physical environmental variables of 250 municipal, county, and district local governments were collected and constructed and then reduced and purified through factor analysis. Second, the type was derived by performing cluster analysis on the reduced factor variables and carbon emissions by analysis unit. Finally, the characteristics of each type were analyzed, and the carbon-neutral city planning and applicable carbon-neutral technology fields were proposed according to the characteristics. After the categorization of carbon-neutral cities throughout Korea, six cluster types were derived; cities in each cluster had similar characteristics. This study suggests that solutions for carbon reduction should be applied by comprehensively considering the social, economic, and environmental characteristics of each city. It concludes that regional physical environmental indicators and energy consumption statistics can be used comprehensively to establish effective policies and apply technologies and techniques at the local government level.

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“…Numerous studies have discussed the national [ 9 , 10 ], regional [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ], enterprise [ 18 ], and industrial [ 19 , 20 , 21 , 22 ] accounting methods and frameworks. However, limited studies exist on IPs, and current accounting research on IP CO 2 emissions has not yet produced systematic results.…”

Section: Introductionmentioning

confidence: 99%

CO2 Emissions Inventory and Its Uncertainty Analysis of China’s Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area

Zhang

Liu

Dong

et al. 2022

IJERPH

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The Chinese government has pledged to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Industrial parks are the key to achieving the carbon peak and neutrality in industrial sectors. Establishing the CO2 emissions inventory is the first step to achieve the carbon peak in industrial parks. In this study, a comprehensive CO2 emissions inventory was established for industrial parks, including three parts: energy consumption, industrial process, and waste disposal. We considered scope 1, 2, and 3 emissions and established an uncertainty analysis framework. Accordingly, scope 1 covered the emissions within the park boundary, scope 2 emissions covered those resulting from electricity and heat usage inside the boundary, and scope 3 included those indirect emissions beyond the boundary. The Maanshan Economic and Technological Development Area (MDA), a typical booming national eco-industrial park of China, was chosen for this case study. The results showed that the MDA CO2 emissions increased yearly, from 376,836.57 tons in 2016 to 772,170.93 tons in 2021. From the industrial structure perspective, heavy industry contributed the highest emissions. By dividing the emissions into scope 1, 2, and 3, scope 2 could be identified as the largest emissions source. In addition, we conducted inventory uncertainty analyses incorporated by activity levels, emissions factors, and unspecific factors. Overall, these results may promote the establishment of greenhouse gas accounting standards for Chinese industrial parks.

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Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Cited by 18 publications

References 65 publications

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

Towards Carbon-Neutral Cities: Urban Classification Based on Physical Environment and Carbon Emission Characteristics

CO2 Emissions Inventory and Its Uncertainty Analysis of China’s Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area

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