China's CO2 emissions: An innovative framework for analyzing carbon reduction in sustainable tourism under the guidance of the United Nations' sustainable development goals

Jia, Junsong; Chen, Dilan; Ju, Min; Lu, Chengfang; Zhong, Chenglin

doi:10.1016/j.jclepro.2023.139752

Cited by 12 publications

(2 citation statements)

References 38 publications

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“…Further analysis reveals that the level of agricultural economy is a decisive factor in promoting the increase of CO 2 emissions. Jia et al (2023) [ 25 ] analyzed the overall increasing trend of carbon emissions from tourism through a combination of social network analysis and the log-mean Divisia index. They concluded that the scale of tourists is the main driving force, while energy intensity is the inhibitor factor of greatest significance.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

The impact of green bond issuance on carbon emission intensity and path analysis

Pang,

Wu,

Zhang

2024

PLoS ONE

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Reducing carbon emission intensity is crucial for achieving sustainable development. Carbon emission intensity is expressively affected by the issuance of green bonds. Thus, it is imperative to assess the influence of green bond issuance on carbon emissions and examine their correlation. Such research holds great potential to expedite the overhaul and modernization of businesses and to construct a circular economy system. This paper uses the spatial Durbin model to draw empirical conclusions by using data from 26 provinces in China between 2016 and 2021. Firstly, under different spatial matrices, it has been analyzed that an increase of 1% in the issuance of green bonds leads to a reduction of 0.306% or 0.331% in carbon emission intensity. It shows that green bonds have the potential to substantially reduce carbon intensity. Additionally, the intensity of emissions in the current period is driven by the intensity of emissions in the previous period. Secondly, the analysis of mediated transmission suggests that green bonds can ultimately reduce carbon emission intensity by changing the energy consumption structure or improving the efficiency of green technology innovation. Thirdly, the analysis of heterogeneity shows that the inhibitory effect of green bond issuance on carbon emissions is stronger in less economically developed regions than in economically developed regions. There is a significant inhibitory effect of green bond issuance in neighboring provinces on local carbon emission intensity. This effect is present only in provinces in less economically developed regions and not in economically developed regions.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

The impact of green bond issuance on carbon emission intensity and path analysis

Pang,

Wu,

Zhang

2024

PLoS ONE

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“…Currently, numerous studies have focused on carbon dioxide emissions in national scale. Junsong Jia et al, 2023 conducted a comprehensive analysis of CO 2 emissions from various sectors and stages in China since 1980, employing the extended logarithmic mean Divisia index to decompose CO 2 emissions across multiple industries and stages. In their research on the low-carbon development of China's tourism industry, Junsong Jia et al, 2023 introduced a novel framework for analyzing the sustainability of carbon reduction in the Chinese tourism sector under the guidance of the United Nations Sustainable Development Goals.…”

Section: Spatial Correlation Of Carbon Emissionsmentioning

confidence: 99%

Spatial network structure and influencing factors of carbon emission intensity in Guangdong-Hong Kong-Macao greater bay area

Wei,

Zheng

2024

Front. Environ. Sci.

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Introduction: In response to China’s ambitious dual-carbon goals, this study investigates the spatial correlation and influencing factors of carbon emission intensity within the Guangdong-Hong Kong-Macao Great Bay Area (GBA), a region pivotal for the nation’s energy conservation and emission reduction efforts. Through a comprehensive analysis encompassing the period from 2000 to 2020, this research aims to delineate the spatial dynamics of carbon emissions and identify actionable insights for regional low-carbon development.Methods: Utilizing an integrated methodology comprising spatial autocorrelation analysis, Social Network Analysis (SNA), and the Quadratic Assignment Procedure (QAP), the study analyzes carbon emission data alongside socio-economic variables. These methodologies allow for a nuanced exploration of the spatial correlation structure and the determination of factors influencing carbon emission intensity across the GBA.Results: Findings reveal a cyclical fluctuation in the spatial network of carbon emissions from 2000 to 2020, characterized by varying degrees of cohesion among cities, indicating significant opportunities for spatial optimization. A “core-periphery” pattern emerges, with economically robust cities such as Hong Kong and Macao at the core, and less developed cities like Huizhou and Jiangmen on the periphery. Cities like Guangzhou and Shenzhen play crucial mediator roles. The QAP analysis further identifies six major influencing factors: geographic spatial proximity, economic development level, urbanization rate, industrial configuration, level of technological innovation, and environmental protection efforts, with the latter four having a markedly positive impact on spatial relevance.Discussion: The study’s insights underscore the importance of understanding the spatial dynamics of carbon emissions and the role of socioeconomic factors in shaping these patterns. For policymakers and stakeholders in the GBA, these findings highlight the necessity of targeted intervention strategies that consider both the unique position of cities within the spatial network and the broader socio-economic context. This approach can significantly contribute to achieving China’s dual-carbon objectives, promoting energy conservation, and facilitating the transition to a low-carbon economy.

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Identifying Critical Paths and Drivers of Carbon Emissions From Tourism: An Integrated Analysis Toward Sustainable Tourism

Tan,

Chen,

et al. 2024

Sustainable Development

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Given the highly fragmented structure and strong industrial linkages of the tourism industry, exploring the flow and drivers of carbon transfer is critical for sustainable tourism. This study combines the environmentally extended input–output (EEIO) model with structural decomposition analysis (SDA) and structural path analysis (SPA) to identify the key sectors, paths, and drivers of carbon emissions from tourism. Taking China as a case, the results show that: (1) China's tourism carbon emissions grew from 34.68 Mt. in 2002 to 137.87 Mt. in 2019, with road transport (22.58%), food and beverage services (21.10%), shopping (20.52%), and air transport (14.91%) constituting the primary emitters. (2) Critical supply chain path “source sector → tourism‐related sectors → final demand” spurring carbon growth of tourism primarily centers on the tourism‐related sectors associated with road transport, air transport, food and beverage services, and shopping, where the source sectors mainly come from energy and food supply, manufacturing, transportation, and logistics sectors. (3) Despite considerable sectoral and path‐level heterogeneity, the key driver for inhibiting tourism carbon emissions is direct energy intensity, while final demand remains the main obstacle to low‐carbon tourism. In addition to the limited effect of energy restructuring, the promising role of optimizing the supply of intermediate goods in cubing tourism carbon emissions is revealed. Finally, relevant theoretical and policy implications are discussed.

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China's CO2 emissions: An innovative framework for analyzing carbon reduction in sustainable tourism under the guidance of the United Nations' sustainable development goals

Cited by 12 publications

References 38 publications

The impact of green bond issuance on carbon emission intensity and path analysis

The impact of green bond issuance on carbon emission intensity and path analysis

Spatial network structure and influencing factors of carbon emission intensity in Guangdong-Hong Kong-Macao greater bay area

Identifying Critical Paths and Drivers of Carbon Emissions From Tourism: An Integrated Analysis Toward Sustainable Tourism

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