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Total factor productivity of cultivated land use in China under environmental constraints: temporal and spatial variations and their influencing factors

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Abstract

The continuous decline in the resource and environment-carrying capacity of cultivated land resources has seriously threatened the sustainable supply of cultivated land resources in China. Using the Hicks-Moorsteen total factor productivity index method, we examine the total factor productivity of cultivated land use (CL-TFP) in China from 2003 to 2017 under environmental constraints. We further use a panel Tobit model to estimate the effect of its influencing factors. The results show that the CL-TFP presents a fluctuating upward trend and reaches data envelopment analysis (DEA) efficiency during the sample period. The regional results reveal a significant spatial difference, especially in the mid-west region, which fails to reach DEA efficiency. China’s main cultivated land did not realize economies of scale. The phenomenon of spatial polarization in what we refer to as very low-value areas and very high-value areas is clear, and the changes are gradual. Regarding the determinative influencing factors, results from the panel Tobit model show that cultivated land usage tax and environmental pollution control investment have no significant effect on CL-TFP, while income level and agricultural intermediate consumption do have a positive effect on CL-TFP. The empirical evidence can help policymakers craft and frame effective policies that improve the utilization efficiency of China’s cultivated land resources.

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Notes

  1. As a traditional agricultural country, the demand for crops is continually expanding in China. To promote the increase in crop yields, the amount of fertilizers and pesticides used is vast in agriculture. Because the fertilizer contains a certain amount of phosphorus and nitrogen, the fertilizer into the soil under the long-term use will lead to severely damage the geological structure of the soil and lose the nutritional content. Although fertilizer may promote the improvement of various crop yields to a certain extent, it is not easy to assess the quality of crops under the cultivation of many fertilizers. At the same time, since most of the current pesticides are organic pesticides, the drugs are also rich in many harmful chemical Pests and diseases that affect crop growth are cured to a certain extent, but under the combined action of living, abiotic, and sunlight, it will also cause soil pollution.

  2. The total factor productivity can establish a broader analysis framework incorporating environmental variables; while efficiency only represents the ratio of output to input. Therefore, we use CL-TFP instead of cultivated land use efficiency in this study.

  3. DEA eliminates many subjective factors, making the conclusion very objective. The DEA method is most effective when the relationship between input and output is unclear or not specified. Besides, through the establishment of an evaluation index system, DEA can evaluate the overall state or performance level of a decision unit with multiple inputs and multiple outputs. It can integrate various factors for evaluation and analysis and then obtain the overall efficiency of each decision unit. Quantitative indicators include technical efficiency that reflects the input-output structure and scale efficiency that reveals the economics of the scale.

  4. DPIN3.1 is a software package. See https://economics.uq.edu.au/cepa/software.

  5. Convergence variation (CV) refers to the variation of CL-TFP (the main decomposition indicator: ITE, ISE, RME) of cultivated land r between different regions or provinces, which gradually decreases over time. As in Barro and Sala-I-Martin (1992), we use the following equation to analyze the variation trend of the coefficient of variation (CV) of the CL-TFP and its decomposition indices in each region.

    $$ \mathrm{CV}=\frac{1}{\overline{\mathrm{CL}-\mathrm{TFP}}}{\left[\frac{1}{n}\sum \limits_i\left(\mathrm{CL}-{\mathrm{TFP}}_{it}-\overline{\mathrm{CL}-\mathrm{TFP}}\right)\right]}^{1/2} $$

    Where, CL − TFPit represents the CL-TFP indicators (ITE, ISE, RME) of the i province and city, and \( \overline{CL- TFP} \) represents the average number of CL-TFP of n economies. If the CV shows a decreasing trend over time, there will be convergence for these n economies.

  6. “The Tenth Five-Year Plan” is mainly the tenth five-year plan outline formulated by the Chinese government for economic and social development. It is a grand blueprint for China’s economic and social development from 2001 to 2005. Correspondingly, “The Eleventh Five-Year Plan” is for the period 2006 to 2010, “The Twelfth Five-Year Plan” is for 2011 to 2015, while “The thirteenth Five-Year Plan” is for 2016 to 2020.

  7. ORIGIN Pro2018 is a professional drawing software. The website refers to https://www.originlab.com.

  8. We have carried out a robustness check by estimating the effect of influencing factors on CL-TFP for each year. The result in each year is consistent with the results in Table 4.

  9. The “GDP Championship” refers to a promotion competition designed by the central government for the chief executives of multiple local governments. The winners of the competition will be promoted, and the competition standard will be determined by the GDP growth rate. Here, the local officials involved are chief executives of local governments at all levels. Local government officials are very enthusiastic about the ranking of GDP and related economic indicators. When the central government proposes the GDP growth rate indicator, the local government will race to propose higher development indicators, and there will be layers of decomposition and layering (Zhou 2007).

  10. “Dual land occupation” refers to the dual urban household registration system and land system in China exclude the market’s effective allocation of urban and rural population and land. As a result, migrant workers who cannot enter the city and who cannot settle in the city are unwilling to give up their original houses in the village without compensation. Bases and cultivated land resources have formed a phenomenon of “dual land occupation” for migrant workers in urban and rural areas, which has increased the shortage of cultivated land resources.

Abbreviations

AVA:

The added value of agriculture

CL-TFP:

Total factor productivity of cultivated land use

CV:

Coefficient of variation

DEA:

Data envelopment analysis

DMU:

Decision-making units

EIA:

The effective irrigation area

FU:

Fertilizer use

ITE:

Technical efficiency (input)

ISE:

Scale efficiency (input)

MEP-MLR:

Ministry of Environmental Protection, Ministry of Land and Resources

PAC:

Planting area of crops

PAM:

Total power of agricultural machinery

PFU:

Plastic film use

PU:

Pesticide use

REU:

Rural electricity use

RME:

Range efficiency (input)

TAO:

Total grain output

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Funding

This work was supported by MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No.19YJA630103).

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, No.388 Lumo Road, Wuhan, 430074, Hubei, China

    Jiachao Peng & Ming Yi

  2. Energy Center, Economics Department, University of Auckland, Auckland, New Zealand

    Le Wen

  3. International Education College, South-Central University for Nationalities, Wuhan, China

    Lina Fu

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  1. Jiachao Peng
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  2. Le Wen
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  4. Ming Yi
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Correspondence to Ming Yi.

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Highlights

• Use the Hicks-Moorsteen total factor productivity index method to study CL-TFP under environmental constraints.

• Apply a panel Tobit model to estimate the influencing factors of CL-TFP.

• Find temporal and spatial variations in CL-TFP.

• Find positive effect of income level on CL-TFP.

• Find positive effect of agricultural intermediate consumption on CL-TFP.

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Peng, J., Wen, L., Fu, L. et al. Total factor productivity of cultivated land use in China under environmental constraints: temporal and spatial variations and their influencing factors. Environ Sci Pollut Res 27, 18443–18462 (2020). https://doi.org/10.1007/s11356-020-08264-8

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