Analysis of the optimal spatial distribution of natural gas under ‘transition from coal to gas’ in China

Lin, Jing; Mou, Dunguo

doi:10.1016/j.reseneeco.2021.101259

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…This spatial spillover effect of EIICI can be largely explained below. First, due to the geographical proximity of natural resource distribution, adjacent areas generally have similar industrial raw materials (e.g., coal and iron ore) [ 65 ], thus laying a natural basis for similar industrial structures. Moreover, adjacent areas are generally in the same regional economic division system, while having commonalities in production costs, markets, as well as technologies.…”

Section: Estimation Results and Discussion Of Driversmentioning

confidence: 99%

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019

Shen²,

Liu³

2022

IJERPH

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China has been reported as the world’s largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China’s EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of “high in the west and low in the east” and “high in the north and low in the south” spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

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Section: Estimation Results and Discussion Of Driversmentioning

confidence: 99%

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019

Shen²,

Liu³

2022

IJERPH

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“…Ye et al 8 also studied natural gas security from the perspective of supply and demand, NGSI was formulated, and provided a reference method for energy security evaluation for countries with different policies. Considering seasonal fluctuations in natural gas demand, Lin et al 9 constructed a model of China's natural gas distribution system, there were four main factors: the perspectives of supply, demand, transportation system and pricing mechanism. SD approach has been drawn up for review all aspects of the natural gas system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Transfer volume from the province IF THEN ELSE(consumption − conventional natural gas production − shale gas production + transfer volume from the province ≥0 , consumption − conventional natural gas production-shale gas production+transfer volume from the province, 0) ×10 9…”

Section: Main Variable Equation Unitmentioning

confidence: 99%

A system dynamic model for natural gas safety supply and demand balance mechanism

Hong,

Sha,

Qian

2023

Energy Science & Engineering

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The security of natural gas supply and demand has always been an important topic, and as a result, analyzing and predicting the supply and demand of natural gas has received considerable attention from researchers. Numerous studies have extensively researched the supply and demand of natural gas. The main purpose of this study is to explore the supply‐demand balance mechanism for natural gas security. A system dynamics model is constructed to assess the supply and demand of natural gas in Sichuan Province, China. A new system dynamics model based on domestic gas, production gas, ecological environment and natural gas supply is proposed. The study considers four scenarios: natural development, economic priority development, environmental priority development and coordinated development. The results show that the rapid economic development has led to an excessive environmental carrying capacity, a larger gap between natural gas supply and demand, a decline in the quality of life of residents, and even affected the energy security of Sichuan Province and China. The findings also show that people's overprotection of the environment can lead to slow economic development, which prevents it from achieving its desired goals. The coordinated development scenario in this paper not only ensures the production of natural gas and the quality of life of residents, but also is an optimal scheme with low energy consumption and pollution. According to the research conclusions, considering the overall situation of China's energy security and the situation in Sichuan Province, the author proposes reforms from the supply and demand sides of natural gas.

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“…Intuitively, the clearest case for switching from coal to gas comes when there is a possibility to use pre-existing infrastructure to provide similar energy services but with lower emissions. A significant NG transmission and distribution pipeline system and spare fuel supply chain capacity must already exist to support the transition while avoiding further high-carbon infrastructure lock-in through the build-out of new pipelines. , So, if both the relative price of coal vs NG and regulatory support switching, then geographical constraint, in the form of gas pipeline availability, can be critical to the feasibility of coal-to-gas switching and has never been thoroughly discussed before.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Availability Limits on the Feasibility of Global Coal-to-Gas Switching in the Power Sector

Yang

Hastings‐Simon

Ravikumar

2022

Environ. Sci. Technol.

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Coal-to-gas switching in the power sector, as happening in the U.S., has been a key driver of near-term greenhouse gas emissions reductions. Can this success be replicated around the world? Here, we explore the limits of a global, plant-level, coal-to-gas transition arising from pipeline availability constraints. Globally, only 43% of coal capacity is within 14 km of a nearby pipeline, the median distance for recent coal-to-gas conversions. Furthermore, plant-to-pipeline distance distributions vary widely–only 30% of coal capacity in India is within 14 km of a nearby pipeline. Most global coal fleets are in the intermediate space of balancing two competing interests–having a young coal fleet with high “avoided” emissions potential through coal-to-gas switching but without access to low-cost gas resources. A global stocktake based on coal fleet age, pipeline access, and natural gas supply security suggests that a coal-to-gas transition in the power sector is unlikely to be a universal climate solution.

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Analysis of the optimal spatial distribution of natural gas under ‘transition from coal to gas’ in China

Cited by 17 publications

References 13 publications

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019

A system dynamic model for natural gas safety supply and demand balance mechanism

Pipeline Availability Limits on the Feasibility of Global Coal-to-Gas Switching in the Power Sector

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