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Rural financial development and achieving an agricultural carbon emissions peak: an empirical analysis of Henan Province, China

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Abstract

Few studies have investigated agricultural carbon emissions peaks, especially from the financial development perspective. This study focuses on the effects of financial scale and efficiency to achieve an agricultural carbon emissions peak in China’s Henan Province. Using an extended STIRPAT model and scenario analysis, we find: (1) with the inverted U-shaped influence mechanism of rural financial scale and incorporating rural financial efficiency, agricultural carbon emissions in Henan will peak at 14.17 million tons in 2040, 12.50 million tons in 2034, and 11.73 million tons in 2023 under the baseline low-carbon, moderately accelerated low-carbon, and stringent low-carbon development scenarios; (2) without financial efficiency improvements, carbon emissions will peak five years, three years, and six years later, and the peak value will increase 0.97 million tons, 0.65 million tons, and 0.30 million tons. Therefore, agricultural carbon emissions will peak earlier with continuous and strong financial policy adjustment to facilitate financial development. We also find that agricultural carbon absorption exceeds emissions, achieving carbon neutrality. The policy implication is that financial development matters to sustainable agricultural development. Developing countries could learn from the financial development experience (i.e., gradual financial reform and a stable financial environment) in Henan and China in general.

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Notes

  1. Reducing carbon intensity need not necessarily reduce carbon emissions if it is achieved by a sufficiently large increase in output. However, carbon intensity is relatively easy to measure and was the target goal for 2020.

  2. The total power of agricultural machinery refers to the total power of various power machinery mainly used in agriculture, forestry, animal husbandry and fishery, which includes farming machinery, irrigation and drainage machinery, harvesting machinery, agricultural transport machinery, plant protection machinery, animal husbandry machinery, forestry machinery, fishery machinery, and other agricultural machinery. It is calculated in watts in the Statistical Yearbook.

  3. Inclusive finance refers to the provision of appropriate and effective financial services at affordable costs for all sectors and groups of society with financial services needs. Small and microenterprises, farmers, low-income urban groups, and other vulnerable groups are its key service focus. China has kept reforming its financial system to push financial sector to serve real economy, fulfill social responsibilities, and facilitate green finance development.

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Acknowledgements

We gratefully acknowledge the following financial support: the Soft Science Research Project of Henan Province (212400410523) and the major project of the School of Economics in Henan University “Research on ecological civilization economy in the new era.”

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Authors and Affiliations

  1. School of Economics, Henan University, Kaifeng Henan, 475004, China

    Guangyue Xu

  2. School of Mathematics and Statistics, Kashgar University, the Xinjiang Uygur Autonomous Region, Kashgar City, 844000, China

    Juanjuan Li

  3. Economics Department, The Belk College of Business and Associate, Energy Production and Infrastructure Center (EPIC), University of North Carolina at Charlotte, Charlotte, 28223, NC, USA

    Peter M. Schwarz

  4. Information Institute of Emergency Management & China Coal Information Institute, Beijing, 100029, China

    Hualiu Yang

  5. School of Economics, Henan University, Kaifeng Henan, 475004, China

    Huiying Chang

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  1. Guangyue Xu
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Appendices

Appendix 1: Agricultural carbon emissions sources and their coefficients

See Tables

Table 4 Major agricultural carbon emissions sources and their coefficients
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4,

Table 5 Carbon emissions coefficient of animals (in kg per unit per year)
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5.

Appendix 2: Results of unit root tests and Johansen cointegration

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Table 6 Results of unit root tests
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6,

Table 7 Johansen cointegration test results
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7,

Table 8 Number of cointegration relationships in Eqs. (1) through (7)
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8.

Appendix 3: Modeling and comparisons

See Table

Table 9 Model Comparison
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9.

First, in equation (C1) we include the impacts of population, agricultural development level, agricultural structure, and the level of agricultural machinery on carbon emissions:

$$L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \theta_{t}$$
(C1)

where AP denotes population level, GDP denotes agricultural outputs, AS denotes agricultural structure, and AM denotes agricultural machinery level.

In equation (C2), we add the efficiency and scale effects of rural finance in order to test the effects of finance scale (FS) on agricultural carbon emissions in Henan:

$$L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \beta_{5} LnFS_{t} + \theta_{t}$$
(C2)

In equation (C3), we add a squared term of rural finance scale to test for a possible inverted-U-curve relationship between finance scale and carbon emissions:

$$L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAp_{t} + \beta_{2} LnGdp_{t} + \beta_{3} LnAs_{t} + \beta_{4} LnAm_{t} + \beta_{5} LnFs_{t} + \beta_{6} Ln\left( {Fs} \right)^{2} + \theta_{t}$$
(C3)

We then add a finance efficiency (FE) variable to get equation (C4):

$$\begin{gathered} L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \beta_{5} LnFS_{t} + \beta_{6} LnFE_{t} + \beta_{7} Ln\left( {FS_{t} } \right)^{2} \hfill \\ + \theta_{t} \hfill \\ \end{gathered}$$
(C4)

We add rural finance efficiency (FE) in the model (C1) to get equation (C5) to test its effects on carbon emissions:

$$L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \beta_{5} LnFE_{t} + \theta_{t}$$
(C5)

We then add its squared term to test a possible inverted-U curve between rural finance efficiency and carbon emissions (see Equation (C6)).

$$L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \beta_{5} LnFE_{t} + \beta_{6} Ln\left( {FE_{t} } \right)^{2} + \theta_{t}$$
(C6)

We add rural finance scale into model (C6) to get model (C7):

$$\begin{gathered} L{\text{nC}}_{{\text{t}}} = Lna + \beta_{1} LnAP_{t} + \beta_{2} LnGDP_{t} + \beta_{3} LnAS_{t} + \beta_{4} LnAM_{t} + \beta_{5} LnFE_{t} + \beta_{6} LnFS_{t} + \beta_{7} Ln\left( {FE_{t} } \right)^{2} \hfill \\ + \theta_{t} \hfill \\ \end{gathered}$$
(C7)

We regress this model and compare the results in Table 9.

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Xu, G., Li, J., Schwarz, P.M. et al. Rural financial development and achieving an agricultural carbon emissions peak: an empirical analysis of Henan Province, China. Environ Dev Sustain 24, 12936–12962 (2022). https://doi.org/10.1007/s10668-021-01976-y

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