HCPD-CA: high-resolution climate projection dataset in central Asia

Qiu, Yuan; Feng, Jinming; Zeng, Yan; Wang, Jun

doi:10.5194/essd-14-2195-2022

Cited by 7 publications

(1 citation statement)

References 59 publications

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“…Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively, while researches on carbon emissions mainly focuses on a specific industry (Song, 2012;Abul et al, 2015;Mikko et al, 2016;Long and Han, 2020;Yuan et al, 2020) and the measures of energy saving and emission reduction (Liu et al, 2015;Hu J. B. et al, 2021;Song et al, 2022).…”

Section: Factor Decomposition Of Industrial Carbon Emissionsmentioning

confidence: 99%

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Liu,

Chang,

et al. 2023

Front. Environ. Sci.

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Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

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Section: Factor Decomposition Of Industrial Carbon Emissionsmentioning

confidence: 99%

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Liu,

Chang,

et al. 2023

Front. Environ. Sci.

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High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

et al. 2022

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To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

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Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

Chang

Yan

et al. 2023

Environ Sci Pollut Res

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Since the carbon peaking and carbon neutrality goals was included into the ecological civilization construction system, every province and city in China have been actively released their local the carbon peaking and carbon neutrality plans for the "14th Five-Year Plan". To address the problems of slow updating of carbon emission data and low accuracy of traditional forecasting models, this paper used data from Chongqing, China, to conduct a study on the subject. this paper measured carbon emissions according to the IPCC method and assessing the development process of resources and environment by means of decoupling analysis. The important factors in uencing carbon emissions are selected by the grey correlation method, and the scenario forecast indicators are constructed according to the relevant policy documents of Chongqing, and the important factors and the consumption of coal, oil and natural gas are taken as the inputs of a single forecast model. The following conclusions were obtained: by comparison, the PSO-ELM model is the best model for predicting carbon emissions in Chongqing. The following conclusions were obtained: the combined PSO-ELM prediction model has lower prediction error and higher accuracy, and is more suitable for carbon emission research. The prediction results show that the carbon emissions in Chongqing during the "14th Five-Year Plan" still maintain upward trend, but the growth rate has slowed down compared with 1998-2018, and the carbon emissions tend to stabilize. Overall, there is a weak decoupling between carbon emissions and GDP in Chongqing from 1998 to 2025.

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HCPD-CA: high-resolution climate projection dataset in central Asia

Cited by 7 publications

References 59 publications

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

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