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Article

How Does Digital Inclusive Finance Policy Affect the Carbon Emission Intensity of Industrial Land in the Yangtze River Economic Belt of China? Evidence from Intermediary and Threshold Effects

by
Linlin Wang
1,
Zixin Zhou
2,
Yi Chen
3,
Liangen Zeng
4,* and
Linlin Dai
5,*
1
School of Government and Public Affairs, Communication University of China, Beijing 100024, China
2
School of Economics, Minzu University of China, Beijing 100081, China
3
Economics School, Guangxi University, Nanning 530004, China
4
School of Public Policy and Management, Guangxi University, Nanning 530004, China
5
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
*
Authors to whom correspondence should be addressed.
Land 2024, 13(8), 1127; https://doi.org/10.3390/land13081127
Submission received: 6 June 2024 / Revised: 18 July 2024 / Accepted: 19 July 2024 / Published: 24 July 2024
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Abstract

:
Digital inclusive finance (DIF) is a strategic tool that fosters the green transformation of the industrial economy. Based on the data from the 11 provinces and municipalities in the Yangtze River Economic Belt of China between 2012 and 2021, This paper utilizes the Tobit, intermediary effect, and threshold effect models to empirically study the impact of DIF on the industrial land carbon emission intensity (ILCEI). This paper reaches the following conclusions: (1) The ILCEI in the region revealed a downward trend during the study period. There are substantial differences in carbon ILCEI; higher upstream and lower downstream. The average ILCEI in the lower reach provinces is 0.5829 ton/m2 during the research period, while that in the upper reach region is 1.0104 ton/m2. (2) DIF has a significantly inhibitory effect on the ILCEI; this effect has nonlinear characteristics. The impact of DIF on ILCEI exhibits a marginally diminishing trend as the industrial land economic agglomeration degree improves. (3) Regarding the transmission mechanism, the level of industrial R&D investment plays a primary intermediary role in the impact of DIF on ILCEI. (4) Concerning control variables, foreign investment dependence and trade contribute significantly to inhibiting ILCEI. Lastly, this paper proposes a series of measures to promote DIF to fully utilize the emission reduction effect. The research outcomes have substantial implications for the sustainable development of industrial land.

1. Introduction

Over the past decades, greenhouse gases emissions have emerged as the key factor triggering global warming and extreme weather events [1,2]. Greenhouse gas emissions are rising globally, which increased by 50.6% from 1990 to 2020 [3,4]. Since the industrial revolution, the global average temperature has increased by over 1.45 ± 0.12 °C [5]. Among all of global greenhouse gases, over 90% belongs to CO2 [6], so controlling CO2 emissions has become the consensus of the world community [7,8]. China has pledged that by 2030, it will reach its peak of carbon emission and that by 2060, it will have achieved carbon neutrality [9].
Land resources provide the essential place for human survival and development [10,11,12,13]. The land use change from human activities has increasingly affected the global ecosystems, a key factor that distributes greenhouse gas emissions. Between 1850 to 2017, changes in land use accounted for one-third of global carbon emissions [14]. Industrial land is an indispensable element of industrial production and an essential part of the promotion of industrialization. Rapid industrial land expansion destroys the carbon sequestration process of natural land and consumes large amounts of energy, resulting in increased carbon emissions [15,16].
With the tactical rise of Central China and the implementation of the Western development strategy, the Yangtze River Economic Belt (YREB) of China has achieved rapid industrial development. From 2004 to 2021, the added value of the industrial sector in the YREB rose from RMB 2.75 trillion to RMB 17.23 trillion, multiplying by 5.3 [17]. However, the YREB has endured the prolonged pressure of both industrial land supply and industrial emission reduction. The industrial land of the YREB grew from 3997.36 km2 to 4835.88 km2 from 2012 to 2021. In 2021, the industrial sector in the YREB emitted 30.58 billion tons of carbon dioxide, accounting for 31.1% of the country’s emissions [18]. Industrial land carbon emission intensity (ILCEI) reflects the industrial carbon emissions per unit area of industrial land. A high ILCEI means that industrial production consumes a lot of energy and emits a lot of industrial carbon emissions per unit of industrial land, which is not conducive to addressing the challenges of global climate change. Therefore, The YREBC strives to, and shall, take appropriate measures to inhibit ILCEI and achieve the national goal of reaching the carbon peak and mitigating the heat island effect in industrial areas [19].
Inclusive finance represents a financial system aiming at providing marginalized groups’ access to financial tools and services, excluded from the traditional financial system [20]. With the advancement of information technology, digital inclusive finance (DIF) has emerged, which is a financial service that integrates digital technologies such as mobile Internet, big data, artificial intelligence, cloud computing, blockchain, and other digital technologies with traditional financial service models. DIF supports financial services like payment, finance, investment, and financing [21], and facilitates the action of inclusive finance. It reduces the financing cost, increases the financing availability, creates new trading markets, and has the potential to promote green technological innovation. Additionally, DIF improves energy efficiency, thereby reducing regional carbon emissions. Many scholars support this concept. Zhao et al. [22], Yang et al. [23], Su and Cao [24], Jiang [25], and Lee and Wang [26] agreed that the DIF can inhibit the carbon emission intensity in China. Furthermore, Yu et al. [27] found that inclusive digital finance has a more profound impact on lowering CO2 emissions in highly industrialized, high-income cities based on the data of 60 emerging and nonemerging economies. Referring to the data of BRICS countries, Pu et al. [28] found that financial technologies contribute to lower emissions and support environmental sustainability. Alsedrah [29] agreed that an increase in green financial practices, fintech adoption, and the use of renewable energy help mineral-rich countries reduce CO2 emissions. However, since DIF aids and boosts manufacturing and industrial activities, it can lead to a higher level of carbon emissions that, in turn, increase global warming. Furthermore, DIF also fosters economic activities, which increases carbon emissions by increasing the demand for polluting energy sources. Cheng et al. [30] supported that DIF significantly promotes urban carbon emissions by increasing resident consumption. Le and Le [31] studied a sample of 31 Asian countries and found that financial inclusion appears to have led to higher emissions of CO2 in the region. In general, the existing literature lacks research on the impact of DIF on the ILCEI, particularly in the YREB of China, a region witnessing rapid industrialization with the characteristics of rapid growth of industrial carbon emissions and land.
Based on the provincial data of the YREB from 2011 to 2021, this paper intends to theoretically analyze and empirically investigate the impact of DIF on ILCEI. The main contributions are as follows: (1) This paper investigates the effects of DIF on ILCEI. in the Yangtze River Economic Belt (YREB), a region undergoing rapid industrialization. It contributes to the advancement and practice of DIF, fostering sustainable green development in the industrial sector. (2) This paper examines the direct impact of DIF on ILCEI, and uncovers the intermediary mechanism between them, highlighting the catalytic role of the industrial R&D investment while expounding on how DIF specifically affects ILCEI. (3) This paper adopts a threshold effect model to probe the nonlinear impact of DIF on ILCEI, providing a valuable reference for policymakers to formulate scientific carbon emission policies across different periods.

2. Literature Review and Research Hypotheses

2.1. The Direct Effect of DIF on ILCEI

DIF can significantly inhibit ILCEI. Firstly, by relying on big data technologies, there is an integration between traditional financial services and digital technologies. DIF can effectively allocate capital and land resources, such as information asymmetry, high transaction costs, and limited mortgage loans, DIF can overcome traditional finance limitations. By doing so, financial services are oriented towards supporting the low-carbon industry in investments [32]. Secondly, DIF can optimize internet technology to collect faster and more accurate data about consumers and producers, using big data’s processing and analysis ability to guide consumers to cultivate low-carbon consumption habits [33,34]. Thus, DIF can propel industrial enterprises to undertake low-carbon transformations. Thirdly, the development of DIF promotes industrial internet and digital manufacturing, enabling the transition from traditional production methods highly dependent on natural resources towards a low-carbon emission model [35]. By utilizing its technical advantage, DIF can revolutionize industrial production and local traditional industry, particularly for those high-emission industries, and foster low-carbon transformation in regional industrial structure. This transformation will effectively reduce energy intensity and carbon emissions.
Hypothesis 1. 
DIF is a key factor helping to mitigate the ILCEI in the YREB.
There is a nonlinear impact of DIF on ILCEI with industrial land economic agglomeration degree (ILEAD) as the threshold variable. In the preliminary stages, the ILEAD is at a limited level, where the development of DIF can satisfy the financial needs of industrial enterprises and inhibit the ILCEI. However, the inhibitory effect of DIF on ILCEI may weaken as the ILEAD evolves. The emission reduction pressure inevitably intensified over time as the ILEAD grew. When ILEAD reaches an advanced stage of development, the limited financial resources may be insufficient for the financing needs of more industrial enterprises. This may hinder their capacity to develop new low-carbon technologies and products, thus weakening the driving force of DIF on industrial carbon emissions.
Hypothesis 2. 
The impact of DIF on ILCEI in the YREB has nonlinear characteristics.

2.2. The Indirect Effect of DIF on ILCEI

Technological innovation in industrial emission reduction can significantly accelerate low-carbon transformation and mitigate ILCEI. However, such innovation requires a long cycle and a high input of industrial R&D, which may be characterized by high sunk costs [36,37,38]. DIF may provide significant support to the industrial R&D investment. Firstly, with greater precision, DIF can meet the financial needs of industrial research institutions and researchers through big data, cloud computing, and other technical means. This is attainable by optimizing the service process and enhancing service efficiency, thus reducing financial constraints on low-carbon transformation in the industrial sectors. Secondly, DIF can substantially reduce the cost of equity and debt financing for industrial enterprises, giving researchers easier access to credit facilities, financing options, and other financial support, propelling technological progress in industrial emission reduction [39]. Thirdly, DIF enables the optimal allocation of financial resources, and, through risk assessment, allocates the limited funds in the research projects that offer higher environmental and economic benefits [40,41,42,43]. This will optimize the allocation and utilization of research funds, and facilitate the development and application of scientific discoveries.
The growth of R&D funding has provided significant support for technological innovation and application of industrial emission reduction [44], like efficient energy utilization technologies, low-carbon material research and development, and carbon capture and storage technologies, which will directly inhibit ILCEI. The growth of R&D funding promotes optimization and upgrading of the industrial structure. The dependence on high-carbon industrial emissions can be reduced and the low-carbon industrial structure can be realized by supporting the development of high-tech industries and green industries [45]. This will curb the ILCEI.
Based on the aforementioned theoretical reasoning, it is posited that DIF may curb ILCEI by increasing R&D investment.
Hypothesis 3. 
DIF can inhibit ILCEI by improving industrial R&D investment.

3. Methodology

3.1. Research Region

The YREB of China extends 2.0523 million km2 over a broad area, accounting for 21.4% of the country’s total area. The YREB is made up of 11 provinces: Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan (Figure 1). The YREB has plentiful natural resources, particularly water energy resources.

3.2. Index Selection and Data Sources

For the purpose of this paper, the explained variable set is industrial land carbon emission intensity (ILCEI), with the digital inclusive finance index (DIFI) set as the core explanatory variable. The industrial land economic agglomeration degree (ILEAD) is set as the threshold variable, and the industrial R&D intensity (IRDI) is set as the mediation variable. To control other factors that may affect ILCEI, this paper designated the foreign trade dependence level (FTDL), foreign direct investment (FDI), and environmental regulation (ER) as the control variables (Table 1). This allows for a better assessment of the impact of DIF on ILCEI.

3.2.1. Explained Variable

ILCEI is equal to the proportion of industrial carbon emissions to industrial land area (ton/m2) [46]. Inhibiting ILCEI is an inevitable requirement for achieving green industrial development. The data are sourced from the China Urban Construction Statistical Yearbook (2013–2022) [47]. The data on industrial carbon emissions are sourced from China Emission Accounts and Datasets (CEAD) [18].

3.2.2. Core Explanatory Variables

Digital finance generally uses information technologies such as big data, blockchain, and artificial intelligence to provide more convenient, efficient, and prevalent financial services [48]. The Digital Finance Research Center of Peking University has evaluated the provincial DIF index of China, which is widely recognized for its authority, and applied in related research [49,50,51,52,53]. Thus, this index was selected to conduct this study. These data are collated from Institute of Digital Finance, Peking University [54].

3.2.3. Threshold Variable

With the increase in ILCEI, the industrial emission reduction effect of DIF may to a certain extent be affected. For the purpose of this paper, the industrial land economic agglomeration degree (ILEAD) is the threshold variable, which is the ratio of industrial-added value to industrial land area. To avoid the impact of inflation, the industrial-added value is calibrated to the price level of 2012, and the data are derived from the National Bureau of Statistics of China (NBSC) [17].

3.2.4. Mediation Variable

DIF promotes the growth and effective use of industrial R&D funds by providing financial support and technical means; more input of industrial R&D funds can amplify the promotion of technological progress in emission reduction. In this paper, industrial R&D intensity (IRDI) is the mediation variable between DIF and ILCEI. This is the ratio of industrial R&D expenditure to GDP. These data are collated from China Economic Information Data (CEIdata) [55].

3.2.5. Control Variable

Foreign trade dependence level (FTDL): China’s foreign trade is primarily centered around goods trade; thus, the development of foreign trade denotes the increase in industrial production activities. This results in the rise of energy consumption and carbon emissions. On the other hand, the development of foreign trade may launch advanced environmental protection technology and equipment [56,57], which can foster green and low-carbon industrial production. To measure the foreign trade dependence level, this paper applies the proportion of foreign trade to GDP. Its data are based on NBSC [17].
Foreign direct investment (FDI): To reduce carbon emissions, the introduction of foreign capital is usually accompanied by the introduction of new production technology, equipment, and management experience [58,59,60]. However, foreign investment in carbon-intensive industries usually results in higher industrial carbon emissions. The control variable of foreign direct investment is measured by the proportion of the total amount of foreign direct investment to GDP (%). These data are issued by CEIdata [55].
Environmental regulation (ER): Government-implemented environmental regulation can obligate industrial enterprises to comply with environmental laws and regulations. These enterprises must research or deploy low-carbon eco-friendly production technology [61], which can inhibit the ILCEI. This paper utilizes the proportion of investment in industrial pollution control to industrial added value to measure environmental regulation. Data are sourced from NBSC [17].
Table 1. All variables.
Table 1. All variables.
Variable TypeVariable NameDefinitionData Source
Explained variableIndustrial land carbon emission intensity (ILCEI)Proportion of industrial carbon emissions to industrial land area (ton/m2).China Urban Construction Statistical Yearbook (2013–2022) [47] and CEAD [18]
Core explanatory variableDigital inclusive finance index (DIFI)Digital Inclusive Finance Index of Peking University.Institute of Digital Finance, Peking University [54]
Threshold variableIndustrial land economic agglomeration degree (ILEAD)Proportion of industrial added value to land area (RMB/km2).China Urban Construction Statistical Yearbook (2013–2022) [47] and NBSC [17]
Mediation variableIndustrial R&D intensity (IRDI)Proportion of industrial R&D expenditure to GDP (%).CEIdata [55]
Control variableForeign trade dependence level (FTDL)Proportion of foreign trade to GDP (%).NBSC [17]
Foreign direct investment (FDI)Proportion of foreign direct investment to GDP (%).CEIdata [55]
Environmental regulation (ER)Proportion of investment in industrial pollution control to industrial added value (%).NBSC [17]

3.3. Methods

3.3.1. Tobit Model

This paper applies the mediation effects model to quantify the impact of DIF on ILCEI. As the ILCEI is a limited dependent variable, the parameter estimation result of the ordinary least squares method may have bias [62,63,64]. The Tobit model can effectively deal with this problem by applying the principle of maximum likelihood estimation [65,66,67]. Thus, the form of the Tobit model is as follows:
Y i t * = a i t + β T X i t + ε i t Y i t = Y i t * if Y i t * > 0 0 if Y i t * ≤ 0
where Y stands for the explained variable, X indicates the explanatory variables, β denotes the parameter to be evaluated, a is the constant term, and ε refers to the stochastic error.

3.3.2. Threshold Regression Model

To test the nonlinear influences of DIF on ILCEI based on the standard threshold model mentioned by Hansen [68], this paper builds a single-threshold regression model:
Yit = α + β1Xit I(qit < η) + β2Xit I(qit ≥ η) + βnZnit + μi + vt + εit       εi,t ~ N(0, σ2i,t In)
where I(·) indicates the indicator function, and qit is the threshold variable. η stands for the threshold value to be estimated. βn(i=1,2,…; n) indicates the parameters to be estimated. u and v represent the individual and time effects, respectively. ε is the random disturbance.

4. Characteristics of ILCEI

The characteristics of ILCEI are shown in Table 2 and Figure 2. The regional ILCEI reduced from 0.7391 ton/m2 in 2021 to 1.108 ton/m2 in 2012 (Figure 3 and Figure 4). After the 18th CPC National Congress in 2012, China strengthened its industrial development and implemented several emission reduction policies. Impacted by this development, the ILCEI level in the YREB, which had previously exceeded the standard, exhibited a significant downward trend after 2012. From a provincial perspective, Shanghai has the lowest level of ILCEI, with an annual average ILCEI below 0.25 ton/m2. This has become the precedent for other provinces to follow to utilize land resources, reduce carbon emissions, and promote sustainable industrial development. This is followed by Hubei, Zhejiang, and Chongqing, with an annual average ILCEI below 0.53 ton/m2. With the development of the economy and an enhanced quality of life, the provincial governments of these provinces have strengthened the environmental framework and enacted more environmental policies to reduce carbon emissions. Hunan, Guizhou, and Yunnan have the highest levels of ILCEI in the YREB, with their annual average ILCEI being higher than 1. In Yunnan, particularly, ILCEI has been higher than 1.2 ton/m2 for several years, which is a contrast to other provinces with low ILCEI. Although this province produced a relatively small emission of industrial CO2, its industrial land scale remains the lowest in the region. Yunnan faces the joint challenge of introducing green production technologies to reduce carbon emissions while strategically expanding its industrial land capacity.
The average ILCEI in the lower reach region is 0.5829 ton/m2 (Table 2), which is higher than that of the entire region (0.7906 ton/m2), the middle reach region (0.7745 ton/m2), and the upper reach region (1.0104 ton/m2). The lower reach region is one of the centers of China’s economy and the hub of capital and technology. It is pursuing a path of high-quality industrial development model, and actively promoting an advanced and sustainable manufacturing industry.
The ILCEI level in the middle reach regions is lower than that of upper reach regions but higher than the lower reach region. Middle reach regions remain the important industrial base of China, producing a lot of carbon emissions. They are adjacent to the eastern coastal areas of China, which is conducive to adopting advanced production technology [46].
The upper reach region is situated in the inland and western regions. Its industrial structures are primarily comprised of traditional labor-intensive and resource-intensive industries. This region has a weak economic foundation and technology accumulation, and struggles to attract advanced manufacturing investments. Utilizing the Western development strategy of China proposed in 1999, the region has undertaken the production transfer from developed eastern areas, with high-emission industries being introduced. This has had negative effects on ILCEI.
Generally, the overall level of ILCEI in middle and upper reach regions is still relatively lower than that of lower reach regions. There is great potential for improving the regional ILCEI in middle and upper reach regions. Concerning future developments, a regulatory framework for the industrial upgrade of carbon emission regulation must be implemented and constantly updated.

5. Regression Analysis

5.1. Benchmark Model

To test whether the DIF can inhibit ILCEI, this research constructs the following Tobit model:
ILCEIit = γ0 + γ1DILit+ γ2FTDLit + γ3FDIit + γ4ERit + μit
To realize the stability and precision of the regression results, this paper conducts a multicollinearity test among all variables by the variance inflation factor (VIF) method. The results reveal that the maximum value of VIF is only 1.35, which is significantly less than the threshold of 10 (Table 3). Therefore, multicollinearity issues do not exist amongst all variables [69,70].
Table 4 reveals the results of the benchmark regression of the impact of the DIF on ILCEI. Column (1) displays the regression outcomes of DIF of ILCEI when the explanatory variable does not include control variables. Columns (2) to (5) shows the regression outcomes after adding each control variable, such as FTDL, FDI, and ER. The results show that the coefficient of the DIF is significantly negative regardless of whether control variables are added or not, indicating that the higher the level of development of the DIF, the lower the ILCEI. Hypothesis 1 is valid. As a new mode of financial service, DIF resolves the problem of financing difficulty and high cost of small and medium-sized enterprises [50,71,72]. This promotes the technological innovation and industrial modernization of enterprises, thus inhibiting ILCEI.
Regarding the control variables, FTDL significantly inhibits ILCEII. The foreign trade structure in the YREB has changed significantly in the last ten years, which has changed from resource products and primary products to mainly mechanical and electrical products. Additionally, to enhance their competitiveness in the international market, industrial enterprises in the YREB have made great efforts to introduce advanced production technology and independent innovation, significantly inhibiting ILCEI. The regression coefficient of FDI is always significantly negative, indicating that the introduction of FDI played a role to suppress ILCEI. The provinces in the YREB raised the threshold for foreign investment. A stricter environmental review mechanism was also established for foreign investment, ensuring that foreign investment met environmental requirements and curbing ILCEI. Although there is a negative correlation between ER and ILCEI, it is not significant. To optimize its emission reduction effect, various coordinated environmental regulation measures are required.

5.2. Robustness Test Model

This study further corroborates the robustness of the empirical results based on three aspects. Firstly, to avoid reciprocal damage between DIF and ILCEI, the endogeneity test is taken through the two-stage least square method. Specifically, this paper uses the lag phase of DIF as an instrument variable, with the two-stage least squares method for an endogeneity test. The test results are illustrated in Table 5. The regression coefficients of DIF on ILCEI regarding whether control variables are added or not are positive and pass the significance test. This is consistent with the test result of the benchmark model; it indicates that the treatment of endogeneity is deemed appropriate. Second, considering the particularity of municipalities, the data from Shanghai and Chongqing were excluded to obtain the results in Table 6 [73]. The estimated coefficient of DIF is also consistent with the benchmark regression model. Third, we added the control variable of R&D personnel (RDP), technology market (TM), and urban population density (UPD) according to the relevant study of Zeng et al. [46]. The definitions of all variables can be found in Zeng et al. [46]. The results of Robustness Test 3 are displayed in Table 7. The estimated coefficient of the DIF on ILCEI remains significantly negative, and consistent with the benchmark regression results. The results of the aforementioned robustness tests all prove that DIF has a significantly negative impact on ILCEI. Therefore, the empirical conclusions of this study are considered to be robust and credible.

5.3. Threshold Effect Test

For each threshold variable, this paper initially considers its possible multithreshold effect. Table 8 indicates a single threshold effect in the model when the ILEAD is selected as the threshold variable.
Table 9 reveals the estimation results with ILEAD as the threshold variable. The effect of DIF on ILCEI is usually negative and statistically significant. However, the inhibiting effect of DIF on the ILCEI varies across the two threshold intervals of ILEAD. Particularly, when the value of InILEAD is less than 3.2900, the coefficient of InDIF is −0.2238 and at the significant level of 1%. In the second spectrum, when 3.2900 ≤ InILEAD, the coefficient is −0.1057 at the 5% significance level. The inhibitory effect of DIF on ILCEI is diminished compared to the initial stage. In summary, with the development of ILEAD, the inhibiting effect of DIF on ILCEI reduces, indicating that DIF has a dynamic and nonlinear impact on ILCEI. Thus, Hypothesis 2 is validated. With the concentration of industrial activities, DIF must prioritize a combination of environmental protection, emission reduction, and other goals while providing more comprehensive financial services.

5.4. Mediation Effect Test

Theoretical analysis suggests that the DIF can affect ILCEI through IRDI. The simulation study found that the Sobel test has higher testing power than the stepwise regression test [74]. This paper constructs the Sobel–Goodman test to assess if the DIF can inhibit ILCEI through the mediation mechanism of IRDI. Table 10 illustrates the test results. Here, the Z values of Sobel, Goodman-1, and Goodman-1 are −4.912, −4.67, and −5.196, respectively. They are all significant at the 1% level, indicating that IRDI has a partial mediating effect due to the influence of DIF on ILCEI. Hypothesis 3 is reinforced by statistical evidence. The development of DIF provides financing guarantees for industrial enterprises and promotes the growth of industrial R&D investment. This fosters the research and development of low-carbon production technology, ultimately hindering ILCEI. This intermediary role optimizes the synergy between finance, technology, and environmental protection, promoting sustainable economic growth and social development.

6. Conclusions

6.1. Main Findings

As a powerful engine that inhibits carbon emissions, the DIF plays a crucial role in supporting China’s carbon peak before 2030 and carbon neutrality before 2060. This study aims to fill the gap in the existing research by providing empirical evidence on how DIF influences ILCEI in the YREB. The paper provides new insights for policymakers and contributes to achieving carbon reduction goals and the sustainable use of industrial land.
This paper analyzed the spatial–temporal features of ILCEI in the YREB of China between 2012 and 2021, and found that the ILCEI in the region revealed a downward trend during the study period, and the regional ILCEI reduced from 0.7391 ton/m2 in 2021 to 1.108 ton/m2 in 2012. There are substantial differences in carbon ILCEI; higher upstream and lower downstream, which coincides with the results of Xie et al. [19]. This paper also explores the impact mechanism and effects of DIF on ILCEI. The analysis of intermediary and threshold effects provides insights into the internal impacts of DIF on ILCEI. Conclusively: (1) The DIF significantly inhibits ILCEI in the basic model. This is evident after using robustness tests. The conclusion confirms Hypothesis 1. Additionally, foreign trade and investment dependence have a notable negative effect on ILCEI. (2) Nonlinear tests indicate that the inhibitory effect of DIF on ILCEI is influenced by ILEAD, exhibiting a gradual decline. Hypothesis 2 is valid. (3) Regarding its impact channels, the DIF promotes the growth of IRDI, which subsequently inhibits ILCEI. Hypothesis 3 is verified.

6.2. Policy Enlightenment

Based on these conclusions, this study proposes the following policy recommendations: (1) The government should promote the coordinated development of DIF across all regions. In the upper reach region with relatively low DIF levels, favorable policies should be implemented and more financial resources should be allocated to promote regional development by the government. In lower and middle reach regions with high DIF levels, regulatory authorities should enhance supervision for development, formulate more effective regulatory policies, and perfect financial risk prevention mechanisms. (2) Financial institutions should actively promote digital finance development, intensify reform and innovation efforts, create more financial products using digital technology, and promote credit resource flow to small and medium-sized industrial enterprises with insufficient credit and high potential for technological innovation. Financial institutions should be incentivized to provide preferential loans and financial products tailored for investments in energy-efficient equipment and renewable energy sources. (3) To strengthen emission reduction efforts in the industrial sector, the government should implement more policies supporting industrial technological innovation. Perfecting the patent protection system and incentivizing the renovation of production equipment in industrial sectors is crucial and can be achieved through targeted fiscal and tax policies, particularly in high-emission sectors like steel, coal power, chemicals, textiles, and machinery. To promote the digital development of the industrial sector and reduce carbon emissions in the industrial production process, industrial enterprises must strengthen cooperation with digital technology companies and increase research input for low-carbon technology. (4) The government should strengthen industrial land use planning, reasonably allocate industrial land resources, and implement stricter environmental regulations in regions with high ILCEI, limiting high-emission industrial projects. This must be executed while strategically expanding industrial land capacity to alleviate the ecological pressure from industrial production. (5) The government should develop green trade, establish export-oriented college technical industrial parks, organize green trade exhibitions and green procurement, and promote international exchanges and cooperation in green products. Industrial enterprises should introduce advanced production technologies and equipment domestically and internationally. (6) The government should set higher entry thresholds for foreign investment and restrict foreign capital inflow into high-emission industrial projects, directing foreign investment into high-tech industries like biomedicine and new materials. Regular evaluation of the implementation effect of green foreign investment projects, and prompt policy revisions are a necessity. (7) The government should be obligated to constantly improve environmental regulations, formulate stricter laws and regulations on environmental protection, and strengthen regulatory oversight ensuring corporate compliance with relevant laws. Penalties for noncompliance should be substantial enough to deter violations. Additionally, establishing and improving the environmental monitoring system is very crucial.

6.3. Limitations and Further Research

The results of this study provide policymakers with empirical evidence on the best ways to mitigate the inhibitory effect of the DIF on ILCEI. Despite the valuable insights provided by this study, it is important to acknowledge its limitations. The implementation of DIF also encompasses micro-individuals such as enterprises and individuals that were excluded from this paper. On the micro level, future research can expand on this paper by exploring the enterprise level, individual level, or industry level. On the macro level, comparing the impact differences of DIF on ILCEI among various regions or countries could offer meaningful reference and guidance for the sustainable development of industrial land. Furthermore, this paper focuses solely on the mediation variables of IRDI and the threshold variable of ILEAD. Prospectively, other variables may be incorporated in the mediation tests and threshold effects. A comprehensive analysis of the spatial spillover in the impact process of DIF on ILCEI also warrants further exploration and investigation.

Author Contributions

L.W.: Conceptualization, methodology, software, validation, formal analysis, writing—original draft preparation, writing—review and editing. Z.Z.: Writing—original draft preparation, writing—review and editing. Y.C.: Writing—original draft preparation, writing—review and editing. L.Z.: Supervision, funding acquisition, writing—original draft preparation, writing—review and editing. L.D.: Writing—original draft preparation, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 52078003) and the Development Research Center of Nanning (No. [2024]FZYJ2-8).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data were obtained from the China official national statistical database.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 13 01127 g001
Figure 1. Study area.
Figure 1. Study area.
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Land 13 01127 g002aLand 13 01127 g002b
Figure 2. The ILCEIs of 11 provinces in the YREB from 2012 to 2021.
Figure 2. The ILCEIs of 11 provinces in the YREB from 2012 to 2021.
Land 13 01127 g002aLand 13 01127 g002b
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Figure 3. The change trend of ILCEIs in the YREB from 2012 to 2022.
Figure 3. The change trend of ILCEIs in the YREB from 2012 to 2022.
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Figure 4. The annual average ILCEIs of each province in the YREB from 2012 to 2021.
Figure 4. The annual average ILCEIs of each province in the YREB from 2012 to 2021.
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Table 2. The ILCEIs of 11 provinces in the YREB from 2011 to 2021 (ton/m2).
Table 2. The ILCEIs of 11 provinces in the YREB from 2011 to 2021 (ton/m2).
Regions2012201320142015201620172018201920202021Mean
Shanghai0.1732 0.1890 0.1699 0.1683 0.2083 0.2076 0.2103 0.2146 0.2065 0.6825 0.2430
Jiangsu0.7108 0.7316 0.6969 0.6724 0.6690 0.6457 0.6539 0.7506 0.6853 0.6103 0.6826
Zhejiang0.5933 0.5560 0.5136 0.5505 0.5393 0.5345 0.5279 0.4672 0.4586 0.4777 0.5219
Anhui0.9650 0.9088 0.9359 0.9117 0.9197 0.8949 0.8773 0.8155 0.8093 0.8029 0.8841
Lower reach region0.6106 0.5964 0.5791 0.5757 0.5841 0.5707 0.5673 0.5620 0.5399 0.6434 0.5829
Jiangxi0.7384 0.8853 0.8775 0.8559 0.7947 1.5481 0.7370 0.7157 0.6717 0.6584 0.8483
Hubei0.6200 0.4847 0.3785 0.4807 0.4979 0.4202 0.3580 0.3539 0.3366 0.6208 0.4551
Hunan1.0402 1.0875 1.2600 1.2044 1.1722 1.0294 0.8979 0.9383 0.8376 0.7339 1.0201
Middle reach region0.7995 0.8191 0.8387 0.8470 0.8216 0.9992 0.6643 0.6693 0.6153 0.6710 0.7745
Chongqing0.7399 0.6031 0.6113 0.5534 0.4931 0.5065 0.5032 0.4582 0.3953 0.3962 0.5260
Sichuan0.7804 0.7131 0.6702 0.6506 0.5534 0.5353 0.4560 0.4883 0.4621 0.4429 0.5752
Guizhou2.1177 1.8857 1.5433 1.3492 1.4400 1.1588 1.1327 1.1817 1.0675 1.2671 1.4144
Yunnan3.7093 1.8417 1.5147 1.2785 1.2509 1.2545 1.3857 1.3639 1.4355 1.4368 1.6472
Upper reach region1.8368 1.2609 1.0849 0.9579 0.9344 0.8638 0.5661 0.8730 0.8401 0.8858 1.0104
The Yangtze River Economic Belt1.1080 0.8988 0.8338 0.7887 0.7762 0.7941 0.7036 0.7043 0.6696 0.7391 0.7906
Table 3. The results of VIF test.
Table 3. The results of VIF test.
DILFTDLFDIERMean VIF
VIF1.021.331.351.021.19
1/VIF0.97800.74930.74180.9627
Table 4. The test results of the benchmark model.
Table 4. The test results of the benchmark model.
Columns (1)Columns (2)Columns (3)Columns (4)
DIF−0.0012 **−0.0013 ***−0.0015 ***−0.0015 ***
FTDL −0.0086 ***−0.0071 ***−0.0074 ***
FDI −0.0735 **−0.0772 **
ER −0.1074
cons1.2065 ***1.4861 ***1.6434 ***1.7057 ***
Note: ** p  <  0.05, *** p  <  0.01.
Table 5. The results of Robustness Test 1.
Table 5. The results of Robustness Test 1.
Columns (5)Columns (6)Columns (7)Columns (8)
DIF−0.0010 ***−0.0014 ***−0.0014 ***−0.0014 ***
FTDL −0.0108 ***−0.0099 ***−0.0096 ***
FDI −0.0304−0.0304
ER −0.0241
cons1.1122 ***1.5406 ***1.6117 ***1.6029 ***
Note: *** p  <  0.01.
Table 6. The results of Robustness Test 2.
Table 6. The results of Robustness Test 2.
Columns (9)Columns (10)Columns (11)Columns (12)
DIF−0.0015 **−0.0016 ***−0.0017 ***−0.0017 ***
GI −0.0011 ***−0.0092 ***−0.0093 ***
FDI −0.1155 ***−0.1227 ***
ER −0.1346 **
cons1.3710 ***1.6133 ***1.8789 ***1.9562 ***
Note: ** p  <  0.05, *** p  <  0.01.
Table 7. The results of Robustness Test 3.
Table 7. The results of Robustness Test 3.
Columns (13)Columns (14)Columns (15)Columns (16)
DIF−0.0015 ***−0.0014 ***−0.0008 ***−0.0012 ***
FTDL−0.0074 ***−0.0068 ***−0.0050 ***−0.0048 ***
FDI−0.0772 **−0.0792 **−0.0708 **−0.1594 ***
ER−0.1074−0.1015−0.0849−0.1190 **
RDP −2.75 × 10−7−3.80 × 10−72.99 × 10−7
TM −0.1006 **−0.1111
UPD 0.0002
cons1.7057 ***1.7033 ***1.5791 ***1.1310 ***
Note: ** p  <  0.05, *** p  <  0.01.
Table 8. The results of the threshold value test (bootstrap = 300 300 300).
Table 8. The results of the threshold value test (bootstrap = 300 300 300).
ThresholdRSS FstatProb
Single2.573062.33 0.000
Double 2.2333 15.210.137
Triple 1.9543 14.280.470
Table 9. The results of the threshold regression model.
Table 9. The results of the threshold regression model.
Coef.Std. Err. t Prob
Threshold value-13.2900
InDIF(InILEAD < 3.2900)−0.2238 ***0.0507−4.4200.000
InDIL(3.2900 ≤ InILEAD)−0.1057 **0.0510−2.0600.042
InFTDL−0.13460.0999−1.3500.181
InFDI0.1516 **0.05192.9200.004
InER−0.01340.0186−0.7200.472
cons0.87250.44381.9700.052
Note: ** p  <  0.05, *** p  <  0.01.
Table 10. The results of the Sobel–Goodman test.
Table 10. The results of the Sobel–Goodman test.
Coef.Std. Err. ZProb
Sobel −0.000570.000116−4.9120.000
Goodman-1−0.000570.000122−4.670.000
Goodman-1−0.000570.000110−5.1960.000
Ratio of mediator effect39.14%
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Wang, L.; Zhou, Z.; Chen, Y.; Zeng, L.; Dai, L. How Does Digital Inclusive Finance Policy Affect the Carbon Emission Intensity of Industrial Land in the Yangtze River Economic Belt of China? Evidence from Intermediary and Threshold Effects. Land 2024, 13, 1127. https://doi.org/10.3390/land13081127

AMA Style

Wang L, Zhou Z, Chen Y, Zeng L, Dai L. How Does Digital Inclusive Finance Policy Affect the Carbon Emission Intensity of Industrial Land in the Yangtze River Economic Belt of China? Evidence from Intermediary and Threshold Effects. Land. 2024; 13(8):1127. https://doi.org/10.3390/land13081127

Chicago/Turabian Style

Wang, Linlin, Zixin Zhou, Yi Chen, Liangen Zeng, and Linlin Dai. 2024. "How Does Digital Inclusive Finance Policy Affect the Carbon Emission Intensity of Industrial Land in the Yangtze River Economic Belt of China? Evidence from Intermediary and Threshold Effects" Land 13, no. 8: 1127. https://doi.org/10.3390/land13081127

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