Spatial disequilibrium and influencing factors of carbon emission intensity of construction land in China

Li, Jia; Jiao, Limin; Li, Fenghang; Lu, Xinhai; Hou, Jiao; Li, Runpeng

doi:10.1016/j.jclepro.2023.136464

Cited by 16 publications

(9 citation statements)

References 65 publications

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“…X 0 is the decision reference point, ∆x is the value of x deviating from the reference point. 3 The weight function describes the decision-maker's subjective perception of probability, which is a probability monotonic increasing function. The formula expression of the decision weight function:…”

Section: Edit Stagementioning

confidence: 99%

“…It mainly includes statistics on gender, age, occupation, marital status, registered residence, nature of housing, whether to have a driver's license and length of service. 3 Investigation of personal travel behavior information. This mainly includes the work address of office workers, departure time for commuting, transportation used for commuting, one-way commuting distance and time, one-way commuting fees, and the number of one-way commuting transfers.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The intensity of transportation plays an important role in suppressing the growth of carbon emissions. Specifically, the contribution value of the transportation intensity factor is negative at 2.1724 million tons, with a contribution rate of 0.9601 [3]. The opportunities and challenges brought by "carbon peak" and "carbon neutrality" to the transportation field not only lie in the transportation itself but also penetrate into different business links in the whole industry chain, including not only transportation equipment manufacturing, aviation, railway, road and shipping and other transportation segments, but also sales, transportation, and other service industries [4].…”

Section: Introductionmentioning

confidence: 99%

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New Energy Commuting Optimization under Low-Carbon Orientation: A Case Study of Xi’an Metropolitan Area

Dai,

Ma,

Zhou

2023

Energies

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Low-carbon travel is an important part of low-carbon cities and low-carbon transportation, and low-carbon transportation is an inevitable choice to slow down the growth of carbon emissions in China. All countries in the world are actively promoting new energy vehicles and attach great importance to the application of the new energy industry in urban transportation. Commuting is an important part of urban life, and the choice of travel behavior has an important impact on traffic and environmental protection. Taking the Xi’an metropolitan area as an example, this paper expounds on the integrated development path of the industrial chain of new energy + travel in the metropolitan area and clarifies the energy transformation model of the integrated development of low-carbon transportation and energy. From the perspective of green and low-carbon, 1000 commuters were interviewed using a questionnaire survey, and the cumulative prospect model was used to verify the internal mechanism affecting commuters in metropolitan areas to choose new energy commuting. The results of the study show that new energy transportation modes play an important role in the low-carbon economy, and under different scenarios and assumptions, there are significant differences in the cumulative prospect values of the subway, new energy buses and fuel private cars, and corresponding optimization measures are proposed to increase the proportion of new energy commuting trips. The results will help further promote the development of a low-carbon economy and energy integration in the field of transportation and provide a reference for the sustainable development of public transportation.

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Section: Edit Stagementioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New Energy Commuting Optimization under Low-Carbon Orientation: A Case Study of Xi’an Metropolitan Area

Dai,

Ma,

Zhou

2023

Energies

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“…Spatial disequilibrium and spatial polarization are closely related and different [14][15][16]. Generally speaking, most studies use a series of inequality indicators to analyze spatial disequilibria, but the use of an inequality index cannot fully reflect the changes in the distribution of the measured indicators [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Study on the Spatial Imbalance and Polarization of Marine Green Aquaculture Efficiency in China

Wang,

Mao,

et al. 2024

Water

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In-depth analyses of the spatial imbalance and polarization of marine green aquaculture efficiency have a profound impact on the realization of high-quality development of fisheries. Based on the data on mariculture in nine coastal provinces of China from 2006 to 2019, this research analyzed the spatial imbalance and polarization of green mariculture efficiency using quantitative measurements and explored their causes. The results showed that (1) the efficiency of marine green aquaculture in China is relatively effective, but there is still room for improvement. The spatial imbalance of the whole country and the three marine economic circles shows an increasing trend, and the efficiency of marine green aquaculture in China has two levels of differentiation characteristics. Compared with the coastal provinces with low and high levels of marine green aquaculture efficiency, the degree of spatial imbalance in the medium-level coastal provinces is greater. In the long run, the efficiency of China’s marine green aquaculture will slowly evolve to a high-level state, and it is particularly important to break the “self-locking trap” of the low-level state. (2) The differences in mariculture yield among coastal provinces are the main factor affecting the spatial imbalance of green mariculture efficiency in China. Promoting the development of low-carbon, resource-saving and high-added-value mariculture products and achieving a win–win situation of economic and ecological benefits is an important means to alleviate the spatial imbalance of China’s mariculture efficiency. (3) There is a certain polarization trend in the efficiency of marine green aquaculture in China, and the polarization degree shows an overall upward trend. Compared with the northern and eastern marine economic circles, the spatial polarization of green aquaculture efficiency in the southern marine economic circle is the lowest. During the observation year, the change in green aquaculture efficiency in coastal provinces did not converge into minority groups, and the main reason for spatial polarization was that there was a large gap in green aquaculture efficiency among coastal provinces. The research results can provide a reference for accelerating the green transformation of mariculture and promoting the high-quality development of fisheries.

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“…According to the research conducted by the Intergovernmental Panel on Climate Change (IPCC), it has been determined that more than 90% of carbon emissions generated by all types of land use can be attributed to construction land [4]. Urban agglomerations have a significant share of construction land due to rapid urbanization.…”

Section: Introductionmentioning

confidence: 99%

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Li,

Liu,

et al. 2023

Land

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With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

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Spatial disequilibrium and influencing factors of carbon emission intensity of construction land in China

Cited by 16 publications

References 65 publications

New Energy Commuting Optimization under Low-Carbon Orientation: A Case Study of Xi’an Metropolitan Area

New Energy Commuting Optimization under Low-Carbon Orientation: A Case Study of Xi’an Metropolitan Area

Study on the Spatial Imbalance and Polarization of Marine Green Aquaculture Efficiency in China

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

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