Abstract

In order to achieve growth in fiscal revenue and the regional economy under the Chinese decentralization system, the land resources misallocation (LRM) among different industries was promoted through the differentiated land supply strategy, which has a vital role in carbon emissions. This study theoretically analyzes the overall effect and the effect of the intermediate LRM mechanism on carbon emissions and empirically tests the impact of LRM on carbon emissions based on panel data collected from 30 provinces in China from 2005 to 2017 using the environmental Kuznets curve theory. The results show that (1) the local governments have monopolized the primary land market across the nation, leading to resource misallocation among industrial, commercial, and residential land. This inefficient and unsustainable allocation aggravated the release of carbon emissions. (2) The impact of LRM on carbon emissions has varied among different regions. LRM in the eastern and central regions significantly exacerbated carbon emissions. A greater impact on carbon emissions occurred in the eastern region, while the impact was insignificant in the western region. (3) There are two mechanisms through which LRM affects carbon emissions. One is the restraint of upgrading industrial structure, and the other is the restriction of technological innovations. In conclusion, speeding up the reform of the tax sharing system is suggested to reduce the excessive dependence of local governments on land resources. Meanwhile, in order to reduce carbon emissions, the land acquisition and transfer system should be reformed to gradually achieve the market-oriented allocation of land resources, and the benefits coordination mechanism of different land transfer modes should be established. Finally, we propose different carbon emission reduction policies for the heterogeneity of regional economic development.

Highlights

  • High consumption of fossil fuels in producing energy has brought about disastrous and irreversible environmental impacts associated with global climate change, posing a huge threat to the world’s sustainable development and preservation of natural resources [1,2,3]

  • Total carbon emissions control is considered as a national strategy in China, and land resource allocation is the critical factor in the study of carbon emissions

  • Based on a theoretical analysis, this paper empirically tested the impact of land resources misallocation (LRM) on carbon emissions using panel data from 30 provinces in China from the perspective of Chinese decentralization from 2005 to 2017

Read more

Summary

Introduction

High consumption of fossil fuels in producing energy has brought about disastrous and irreversible environmental impacts associated with global climate change, posing a huge threat to the world’s sustainable development and preservation of natural resources [1,2,3]. The introduction of industrial structure upgrades and technological innovations as intermediary variables of LRM that affect carbon emissions is used to theoretically explain its mechanism and expand the research on the impacts of local government behaviors on carbon emissions. The main mechanisms associated with LRM to restrain the upgrading of industrial structures are as follows: on the one hand, in order to stand out in the “promotional championship”, local governments have adopted a “competitive” strategy, that is, a low-price and large-scale transfer of industrial land, lower industrial entry barriers, and a bottomline focus on investment quality to attract enterprises to invest. LCEit represents the total carbon emissions, LRMit refers to the land resource misallocation, LEit represents the economic growth level, and LEit is used to verify whether there is a non-linear relationship between economic growth and environmental pollution. ∑nj=1 Xjit is a set of control variables, where j represents the number of control variables, and εit is the error term

Specification of Variables
Data Collection
Basic Estimation Results
Regional Differences
Intermediate Mechanism
Robustness Test
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call