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Analyzing driving forces of China's carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis.

ABSTRACT: Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997-2040. Finally, the Spatial-Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper.

Supplementary information

The online version contains supplementary material available at 10.1007/s10098-021-02240-7.

SUBMITTER: Song C

PROVIDER: S-EPMC8616976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe extent of healthcare expenditure within households stands as a crucial indicator in low and middle-income countries (LMICs). When out-of-pocket healthcare expenses surpass household income or become unduly burdensome, it serves as a significant socio-economic alarm, resulting in a reduced quality of life, a phenomenon referred to as 'catastrophic health expenditure (CHE).' Multiple factors can contribute to the occurrence of CHE. The study's objective was to identify the key uncertainties and driving forces influencing CHE to develop scenarios in Iran on the horizon of 2030.MethodsThis study was conducted between December 2021 and January 2023, data were collected through a literature review, and experts' opinions were gathered via questionnaires, interviews, and expert panels. The statistical population included experts in the fields of health policy, health economics, and futures studies. Scenario Wizard software and MICMAC analysis were employed for data analysis, providing valuable insights into potential future scenarios of health expenditures in Iran.ResultsBased on the results of the scoping review and semi-structured interview, 65 key factors in the fields of economics, politics, technology, social, and environmental were identified. The findings of the MICMAC analysis presented 10 key variables. Finally, six main scenario spaces are depicted using Scenario Wizard. These scenarios included catastrophic cost crises, sanction relief, selective information access, technological ambiguity, induced demand management, and incremental reforms.ConclusionsEach of the six drawn scenarios provides images of the future of health expenditure in Iranian households on the horizon of 2030. The worst-case scenario from all scenarios was scenario one, with the most probable and critical features to derive Iran's health expenditures. The current study is a valuable addition to the literature depicting the key drivers that all developing nations can consider to decrease exposing households to catastrophic and impoverishing health expenditures.

Dataset Information

Analyzing driving forces of China's carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis.

Supplementary information

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