Unlocking the solid waste suppression path from the perspective of zero waste cities: Exploring the inhibitory path of green credit on solid waste

Abstract

Solid waste management is key to achieving zero-waste cities, encompassing waste reduction, recycling and the circular use of resources. This not only alleviates environmental pressure but also serves as a crucial avenue for sustainable development and the protection of natural resources. With the ongoing advancement of zero-waste cities, reducing solid waste generations has become a focal point of interest for policymakers and the academic community alike. Previously, the majority of academic research concentrated on green finance and environmental pollution, with a particular focus on green credit at the micro-level, yet in-depth studies on solid waste management were scarce. To bridge this research gap, this article introduces spatial econometric models, mediating effect models and threshold models, concentrating on analysing the impact pathways of green credit on solid waste management. The study reveals: (1) green credit significantly suppresses solid waste generations in Chinese provinces, but this effect exhibits regional disparities; (2) green credit exhibits a threshold effect in mitigating solid waste generations, implying that an appropriate level of green credit and environmental governance investment enhances its inhibitory effect on solid waste and (3) research and development investment intensity and industrial structure upgrading serve as intermediary factors in the process of green credit inhibiting solid waste. These research conclusions can provide valuable references for local governments and businesses to enhance solid waste management.

Keywords

Green credit solid waste empirical study

Introduction

In 2020, for the first time in human history, the total mass of artificial materials exceeded the total biomass of all living organisms on Earth (Chu et al., 2023; Elhacham et al., 2020). Naturally, this is a terrifying catastrophe and calamity. While traditional industrialization has driven rapid social and economic development by exploiting natural resources on a large scale, it has also led to significant resource waste, environmental pollution, ecological destruction and climate change (Bhat et al., 2023; Xu et al., 2023).

Over the past four decades of reform and opening-up, China has achieved remarkable progress in economic and social development. However, with these achievements, the issue of industrial solid waste has gradually become more severe, characterized by worsening environmental pollution, low resource utilization and a lack of resource conservation awareness (Huo et al., 2023; Tang and Zhou, 2023; Ye et al., 2023; Zhang et al., 2022), drawing significant government attention. Especially in 2022, the production of industrial solid waste in China is estimated to have reached a staggering 4.2 billion tonnes, with a cumulative total exceeding 60 billion tonnes (Solid Waste Annual Report, 2023). This challenge is particularly pronounced in the post-pandemic era, further exacerbated by ongoing industrialization and urbanization and the new development model emphasizing a domestic circulation as the main body supported by the mutual reinforcement of domestic and international circulations. Thus, strengthening industrial solid waste management is not only crucial for addressing current environmental pollution and resource wastage but also supports the nation’s journey towards ecological civilization and high-quality development goals. This has gradually become a core issue of investigation within academic and political circles.

In response to new challenges and in pursuit of innovative solutions, the academic sector, leveraging the foundational principles of environmental science and sustainable development, has embarked on strategic systemic investigations into solid waste. This research predominantly addresses the dual aspects of solid waste: its detrimental effects and the factors essential for its suppression. On the aspect of hazards, scholars highlight the environmental and societal repercussions of inadequate waste handling, including soil, water and air pollution, which threaten ecosystem stability and public health (Geyer et al., 2022; Kaza et al., 2018). The literature emphasizes that mismanaged solid waste not only leads to the loss of valuable recoverable resources, exacerbating resource sustainability challenges (Dipanjana et al., 2023; Hind et al., 2023; Sen Sahu and Kumar, 2023) but also poses significant risks to wildlife and ecological balances through its accumulation (Li et al., 2023a; Sasmoko et al., 2022; Takunda and Steven, 2023). Additionally, waste disposal methods, such as landfilling and incineration, are major sources of greenhouse gas emissions, further impacting global climate change (Couth et al., 2015; Das et al., 2018; Gu et al., 2023; Ke et al., 2023; Mor and Ravindra, 2023; Nagpure et al., 2015; Saikawa et al., 2020). This body of research underscores the urgent need for effective solid waste management practices to protect the environment and ensure sustainability.

In the context of waste mitigation, scholars emphasize several critical factors. Firstly, the formulation and implementation of sustainable waste management policies are considered paramount. These policies can reduce waste generation through measures like regulating waste production, promoting resource recovery and encouraging clean production (Mor and Ravindra, 2023; Tamirat et al., 2023). Secondly, the adoption of efficient waste reduction and recycling technologies is crucial in decreasing both the quantity of solid waste and resource wastage (Chen et al., 2023; Uekert et al., 2023). Raising awareness and engagement among the public and businesses are vital for fostering environmentally friendly behaviours (Kondratenko et al., 2021; Noorhosseini et al., 2019). Additionally, the implementation of a circular economy model is seen as a means to reduce waste emissions, as it emphasizes the reuse and recycling of resources (Kurniawan et al., 2022; Parra-Saad, 2022). Government policies and regulations play a significant role in waste mitigation through the establishment of waste management standards, the implementation of incentive measures and environmental taxation policies (Zhang et al., 2022). Furthermore, social cooperation and partnerships are critical for collaboration among various stakeholders, working collectively towards waste mitigation objectives (Senith et al., 2018). Continuous research and innovation consistently drive technological advancements in waste management and resource recovery fields (Liao et al., 2022). Lastly, the development of new technologies can enhance the efficiency of waste treatment and recycling, reducing resource wastage (Alola et al., 2023). In addition, the academic community believes that green human resources, price policies and recycling rewards, can also motivate individuals and businesses to take more environmentally friendly actions (Awan et al., 2023; Bui et al., 2022; Cui et al., 2022). These elements are interconnected, collectively propelling the achievement of solid waste mitigation.

As research deepens, the academic community has expanded on the factors inhibiting solid waste generations, viewing regional finance as a core carrier of regional economic development that can bring industries low financing costs and diversified financing channels. This promotes technological innovation and industrial upgrading in enterprises, saving non-recyclable resources and reducing solid waste generations (Hu et al., 2019; Li et al., 2022a; Zhang et al., 2023). Despite the growing interest in green credit, systematic research on the specific mechanisms through which it influences solid waste management remains limited. As a key component of the financial system, green credit has gained prominence within the framework of green development strategies. By channelling funds into low-carbon and environmentally friendly projects, green credit supports green industries, reduces waste emissions and fosters both economic and environmental sustainability. Increasingly, the academic community is investigating the impact of green credit on green innovation, industrial structure upgrading, energy structure transformation and environmental pollution (Di et al., 2023; Li et al., 2023b; Lin and Pan, 2023; Zhang et al., 2023). This indirectly reflects the pathways through which green credit affects solid waste. As Salahuddin et al. (2018) have examined Kuwait as their case study, they argue that financial development, by supporting large-scale industrial operations, has expanded the consumption of resources and energy, thereby increasing waste emissions. Although there are differing opinions, the academic consensus on how finance impacts the reduction of waste emissions is converging, providing a research direction for this study.

Research gaps

Synthesizing existing research, the academic community has explored the factors inhibiting solid waste from multiple perspectives, ranging from green technology and policy evolution to green finance (Alola et al., 2023; Uekert et al., 2023). However, there is a noticeable lack of in-depth analysis on how green credit influences solid waste management, highlighting the innovative angle of this article. Firstly, although green credit, as a financial strategy to promote energy conservation, environmental projects and sustainable development (Zhang et al., 2023), has made progress in practice, the academic world lacks systematic thinking regarding its application and effectiveness in solid waste management. Secondly, while green credit is a crucial component of the financial system, its complexity due to the influence of multiple factors has not been fully studied. Lastly, the impact of green credit on solid waste requires mediation variables for its realization and the theoretical exploration of this mechanism needs further deepening. Overlooking these areas of research may limit a comprehensive understanding of the mechanisms by which green credit functions.

Therefore, (1) to clarify the pathways through which green credit affects solid waste management, this article delves into the impact of green credit on solid waste by embedding panel data from various provinces and cities in China and employing spatial econometric models. It highlights the pathways through which green credit influences solid waste management. (2) Furthermore, by incorporating threshold models, this research sketches the marginal conditions under which green credit suppresses solid waste from both internal and external dimensions. (3) By treating research and development (R&D) intensity and the upgrading of industrial structures as mediating variables within a mediation effect model, it elucidates the mechanisms through which green credit suppresses solid waste. This contributes to a broader discussion on sustainable development and green finance.

Marginal contributions

Firstly, incorporating green credit and quantifying its theoretical foundation in relation to solid waste management helps to advance the theoretical boundaries of solid waste governance. Secondly, by considering the influence of both internal and external factors and utilizing a threshold model with green credit and environmental governance investment as threshold variables, it systematically examines the potential threshold effects of green credit in suppressing solid waste. This offers a fresh perspective on unravelling the complexity of this relationship. Lastly, it introduces key variables such as research and development intensity and industrial structure upgrading, with a particular focus on the transmission mechanism of green credit in relation to solid waste. This aids in a deeper comprehension of the intrinsic mechanisms of solid waste management.

The remaining sections of the article are as follows: The second part covers theory and research hypotheses, providing a theoretical foundation for subsequent empirical work. The third part presents the models and data. The fourth, fifth and sixth parts comprise the empirical analysis, where the hypotheses from the article are tested. The seventh part consists of the research conclusions, summarizing the study findings, addressing research limitations and providing future prospects.

Theory and research hypotheses

Direct effect

The relationship between green credit and solid waste has attracted widespread attention in the academic community, especially in the context of building waste-free cities. Although there is currently no empirical research in this area, some important theoretical findings have emerged. Firstly, green credit, by providing financial support, encourages businesses to adopt more environmentally friendly production methods and waste disposal practices, which are expected to reduce the generation of solid waste (Guo and Liu, 2021). Secondly, governments can incentivize financial institutions to provide green credit through policy measures, thereby supporting the development of solid waste management and resource recovery projects (Di et al., 2023; Zhang et al., 2023). Additionally, green credit can reduce the cost of waste disposal for businesses, encouraging them to adopt more environmentally friendly and efficient waste management technologies such as waste recycling and reuse (Li et al., 2023a; Lin and Pan, 2023). Furthermore, green credit, by providing long-term and stable financial support, enables businesses to make long-term environmental investments, potentially reducing solid waste generations (Guo and Liu, 2021; Zhang et al., 2023). In summary, these theoretical findings provide a strong theoretical foundation for further research and form the basis for our first research hypothesis.

H1:Green credit significantly mitigates solid waste generation.

Threshold effect

Threshold variable: Green credit

The effectiveness of green credit in mitigating solid waste generations is not fixed but significantly influenced by the level of green credit investment. Research indicates that when the level of green credit investment is relatively low, its impact on solid waste generations is somewhat limited because companies may lack sufficient funds and resources to adopt more environmentally friendly production technologies (Guo and Liu, 2021; Li et al., 2023a). However, when the level of green credit reaches or surpasses a certain threshold, companies become more proactive in investing in environmental protection, adopting cleaner and sustainable production methods, thereby significantly reducing solid waste generations (Sun and Zeng, 2023). Additionally, government policies play a crucial role in adjusting the effectiveness and threshold level of green credit. Governments can influence companies’ environmental investment levels and, consequently, solid waste mitigation levels by setting environmental standards, providing incentive measures and supporting the development of green credit (Li et al., 2022b). Based on the above discussion, we derive the second research hypothesis in this article:

H2: The effectiveness of green credit in mitigating solid waste is influenced by the level of green credit investment.

Threshold variable: Environmental governance investment

Regarding the role of environmental governance investment in the process of solid waste reduction through green credit, scholars have also started to conduct research. Research results indicate that the effectiveness of green credit in curbing solid waste generations is not static but rather influenced by the level of environmental investment made by enterprises. There exists a threshold level, and only when a company’s environmental investment surpasses this level can green credit have a significant impact (Li et al., 2022a). However, this threshold level varies across different industries, regions and policy environments (Li et al., 2023; Zhang et al., 2023). Government policies can influence the level of environmental investment by setting environmental standards, providing incentives and implementing tax policies, thus adjusting the threshold level. There are also significant differences in the threshold among different industries, with some industries being more sensitive to environmental investment, where even lower levels of investment can yield noticeable effects. As environmental technologies advance and costs decrease, the threshold for environmental investment may decline, allowing more companies to afford the cost of environmental investments (Xu et al., 2023; Zhang et al., 2023). These insights contribute to a deeper understanding of the threshold effect of environmental investment on the role of green credit in solid waste reduction and provide essential theoretical and practical guidance for policy formulation and business decision-making. Based on the above discussion, we derive the third research hypothesis in this article:

H3: The impact of green credit on mitigating solid waste is contingent on the level of environmental governance investment.

Transmission effect

Transmission variable: R&D investment intensity

Green credit plays a crucial role in curbing solid waste generations, with mechanisms that involve providing financial support to encourage businesses to increase R&D investment for developing more environmentally friendly production processes and waste disposal technologies (Benito-Hernández et al., 2023; Guo and Liu, 2021). Incentive policies from both governments and financial institutions also steer businesses to allocate green credit towards R&D, thereby promoting the development of environmental technologies and innovation, reducing solid waste generation and emissions (Ke et al., 2023). Furthermore, as the demand for green innovation within businesses grows, financial institutions may develop more financial products tailored to these sectors, further stimulating R&D investments and reducing solid waste generations (Song et al., 2022). This mechanism contributes to the achievement of waste-free city development goals and represents a significant nexus between green credit and solid waste issues, forming the basis for the forth research hypothesis in this study.

H4: R&D investment intensity serves as an intermediary bridge in the process of green credit’s role in mitigating solid waste.

Transmission variable: Upgrading of industrial structure

Green credit, as a financial instrument in the field of environmental protection, plays a crucial role in promoting industrial upgrades and curbing solid waste generations (Guo and Liu, 2021; Tian et al., 2022). Firstly, green credit provides financial support to enterprises that are willing to adopt clean production technologies and efficient resource utilization. This support helps them upgrade their equipment and innovate their technologies to make production processes more environmentally friendly and optimize resource utilization, thereby reducing waste generation (Ke et al., 2023). Secondly, green credit can also be used to fund the development of emerging environmentally friendly industries, which typically have lower levels of carbon emissions and waste generation, such as the renewable energy and circular economy sectors. By providing financial support to these industries, green credit can stimulate their rapid growth, driving the overall industrial structure towards a more environmentally friendly direction (Kharola et al., 2022). In summary, green credit, through the pathway of enhancing industrial structural upgrades, further suppresses solid waste generations and provides strong support for promoting sustainable development and environmental protection efforts. This leads to the fifth research hypothesis in the article.

H5: Upgrading of industrial structure serves as an intermediary bridge in the process of green credit’s role in mitigating solid waste.

To illustrate the pathways through which green credit affects solid waste and the research hypotheses formulated on this basis, Figure 1 in the article provides a visual summary.

Methods and data

Model selection

Model specification

This study, establishes a panel econometric model, expressed as:

{SW}_{it} = c + β_{1} {GL}_{it} + β_{2} X_{it} + ξ_{i} + θ_{t} + ν_{it}

(1)

In this context, ‘i’ represents provinces and cities, ‘t’ denotes years and ‘SW’ and ‘GL’ respectively stand for solid waste and green credit in province ‘i’ during year ‘t’. The variable ‘X’ encompasses control variables, whereas ‘c’ represents the constant term. The symbol ‘ζ’ reflects individual effects, accounting for unobservable disparities among regions that are challenging to quantify, such as natural resource endowments, industrial structures and factor systems. Meanwhile, ‘θ’ captures time effects, serving to control for the influence of macroeconomic conditions, relevant policies and technological advancements on solid waste across various regions. Finally, ‘V_it’ encompasses disturbance terms.

Threshold model

Building upon Hansen (1999)’s threshold model, with green innovation level and environmental governance investment as threshold values, the focus is on detecting multiple threshold values for green credit in suppressing solid waste. The formula is set as follows:

S W_{i t} = a_{0} + a_{1} GL {X \leq ϕ} + a_{2} GL {X \geq ϕ} + τ X_{i t} + ε_{i t}

(2)

In detail, X is the threshold variable; $φ$ is the threshold value and $α$ represents the model intercept term; $ε_{i t}$ represents model error disturbance term, while the meaning of the remaining variables is unchanged.

Mediation effect model

Introducing the mediation effect model, with industrial structure upgrading and R&D intensity as intermediary variables, the focus is on the mediating mechanism of green credit on solid waste. The formula is as follows:

{SW}_{it} = c + β_{1} {GL}_{it} + β_{2} X_{it} + ξ_{i} + θ_{t} + ν_{it}

(3)

M_{it} = c + β_{1} {GL}_{it} + β_{2} X_{it} + ξ_{i} + θ_{t} + ν_{it}

(4)

{SW}_{it} = c + β_{1} {GL}_{it} + β_{2} M_{i t} + β_{3} X_{it} + ξ_{i} + θ_{t} + ν_{it}

(5)

where M represents the intermediary variable, encompassing R&D intensity and industrial structure upgrading, the rest is consistent with formula (1).

Index selection

Dependent variable: Solid waste

Due to data availability, industrial solid waste discharge volume (in 10,000 tonnes) is used as the measurement indicator to scientifically characterize solid waste. The actual discharge volume is logarithmically transformed to reflect the regional intensity of solid waste generations (Guo and Liu, 2021).

Explanatory variable/threshold variable: Green credit

Aiming to accurately depict green credit adopts the research approach of scholars such as Xie and Liu (2019), employing the following formula:

\begin{array}{l} Green Credit = 1 - 6 ratio of interest expenditure \\ of high energy - consuming industries \\ to total interest expenditure of industrial industries \end{array}

(6)

Threshold variable: Environmental governance investment

Government environmental investment involves financial support from the government to encourage the reduction, resource utilization and harmless treatment of waste, thereby effectively improving environmental quality and achieving sustainable development goals. Based on this, the article characterizes it by the annual investment amount in solid waste management.

Mediating variables

To gain a scientific understanding of the mechanisms through which green finance affects solid waste, the study selects R&D investment intensity and industrial structure upgrading as mediating variables for analysis. These variables are characterized as follows:

Mediating variables 1: Research and development intensity

Higher R&D investments can drive the development of more effective waste treatment technologies and methods, thereby reducing waste generation and enhancing the rate of waste resource utilization. Based on this, the article characterizes it by the percentage of R&D expenditure to the regional gross domestic product.

Mediating variables 2: Upgrading of industrial structure

To clarify the mediating effects of the industrial structure upgrade, the article divides industrial structure upgrade into two dimensions: rationalization of the industrial structure and advancement of the industrial structure. The rationalization of the industrial structure is characterized using the Theil index (Gan et al., 2011), whereas the advancement of the industrial structure is represented using the industrial structure vector angle method as studied by Fu (2010) employing the following formula.

Rationalization of the industrial structure (Theil index):

T = \sum_{i = 1}^{n} (\frac{Y_{i}}{Y}) In (\frac{Y_{i}}{Y})

(7)

where T represents the Theil index; n is the number of industries; Y_i is the output of the ith industry; Y is the total output of all industries; ln denotes the natural logarithm.

The higher the value of the Theil index, the greater the disparity or inequality among industries; conversely, the lower the value, the lower the disparity or inequality.

Advancement of the industrial structure = 3 θ_{1} + 2 θ_{2} + θ_{3}

(8)

θ_{j} = \arccos (\frac{\sum_{i = 1}^{3} x_{i, j} * x_{i, o}}{{(\sum_{i = 1}^{3} x_{i, j}^{2})}^{\frac{1}{2}} * {(\sum_{i = 1}^{3} x_{i, o}^{2})}^{\frac{1}{2}}}), j = 1, 2, 3

(9)

Within this context, $X_{0} = (X_{1, 0}, X_{2, 0}, X_{3, 0})$ signifies a tri-dimensional vector reflecting the ratios of the three primary industries; X1 = (1,0,0), X2 = (0,1,0) and X3 = (0,0,1) constitute the foundational vector set; the measure $θ_{j}$ X denotes the angular separation among them.

Control variables

Control variables encompass urbanization, industrialization level, per capita grain production, agricultural electricity consumption and economic development level, as shown in Table 1.

Table 1.

Control variables.

Control variables	Measuring indicators
IDD	The proportion of industrial value added to regional GDP
Economic development level (GDP)	The logarithm of per capita GDP in a region
FDI	Represented by the actual FDI amount
FD	Local fiscal general budget expenditure (in billions of yuan)/local fiscal general budget revenue (in billions of yuan)

FD: financial decentralization; FDI: foreign direct investment; GDP: gross domestic product; IDD: industrial level.

Data sources

Based on the panel data of 30 provinces and cities from 2007 to 2021, this research comprehensively examined the mechanism of green credit on solid waste of China’s provincial industry. All the data were collected from relevant statistical yearbooks of China. For instance, sources of data include China Statistical Yearbook, Industrial Statistical Yearbook, Financial Statistical Yearbook, etc.

Benchmark regression analysis

Direct effect analysis

Unit root test

Since panel data are used in this article, in order to avoid the problem of spurious regression that may arise from non-stationary variables, unit root tests are conducted on the relevant variables using Hadri, LLC and Fisher tests before model estimation. The test results are shown in Table 2.

Table 2.

Unit root test.

Variables	Hadri	LLC	Fisher	Variables	Hadri	LLC	Fisher
SW	8.566***	−6.160***	−8.753***	FDI	9.527***	3.424	−2.385*
D.SW	8.337**	−6.686	−9.363***	D.FDI	9.646**	−4.564	−3.004*
GL	6.642***	−4.282***	−8.323***	IDD	8.784***	−4.920***	−7.231***
D.GL	7.342***	−4.556***	−7.832***	D.IDD	9.051***	−7.952***	−2.700***
FD	5.967***	−4.320***	−8.140***	GDP	10.324***	−5.459***	−6.339***
D.FD	6.204***	−5.439***	−3.567***	D.GDP	10.051***	−2.236***	−7.542***

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; IDD: industrial level.

p < 0.1. **p < 0.05. ***p < 0.01.

From Table 2, it can be observed that FDI exhibits unit roots, indicating non-stationary series. To further determine the number of unit roots, the article proceeds to conduct unit root tests on the first-order differenced series of all variables, including FDI. The test results show that all the first-order differenced series, including FDI, pass the significance test, signifying the rejection of the null hypothesis of the presence of unit roots. In other words, these series are stationary after differencing.

Cointegration test

In order to further analyse whether there exists a long-term equilibrium relationship among the variables, this article employs the KAO test, Pedroni test and Westerlund panel cointegration test methods for examination, and the results of the tests are shown in Table 3.

Table 3.

Cointegration test.

Method	Statistic	p
Pedroni
Modified Phillips–Perron t	6.1835	0.000
Phillips–Perron t	−7.0946	0.000
Augmented Dickey–Fuller t	−7.3216	0.000
Kao
ADF	−2.8517	0.002
Westerlund
Variance ratio	−1.9675	0.024

The panel cointegration test results from Table 3 indicate that the p-values for the KAO test, Pedroni test and Westerlund test are all less than 0.01, which means that at the 1% significance level, we reject the null hypothesis of no cointegration among the selected variables. This suggests that there is a stable long-term equilibrium relationship among the variables chosen in the study, allowing for regression analysis to be conducted.

Direct effect

From Table 4, it is evident that regardless of whether it is provincial fixed effects, time fixed effects or both fixed effects, green credit effectively restrains solid waste generations. In terms of the intensity of restraint, it shows that time fixed effects > both fixed effects > provincial fixed effects. This further confirms the pathway through which green credit acts on solid waste, and the possible reason is that green credit, by providing funds, promoting innovation, supporting clean production and encouraging compliance, provides enterprises with the incentive and resources to reduce solid waste generations. Thus, this validates the first hypothesis.

Table 4.

Direct effects test.

SW	M1		M2		M3
SW	β	Z	β	Z	β	Z
GL	−0.880***	−6.000	−0.595***	−6.070	−0.689***	−7.220
FDI	0.249***	4.480	−0.041	−1.280	−0.003	−0.070
GDP	−0.526***	−3.280	0.642***	11.100	−0.010	−0.080
IDD	3.928***	19.290	−0.096	−0.490	0.053	0.240
FD	−0.540**	−2.390	0.294*	1.870	0.506**	2.620
_cons	4.853***	6.860	1.126***	4.580	3.923***	7.680
Time control	Yes		No		Yes
Id control	No		Yes		Yes

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; IDD: industrial level.

p < 0.1. **p < 0.05. ***p < 0.01.

Robustness test

Exclude years affected by the COVID-19 pandemic

In light of the impact of the COVID-19 pandemic on China’s industrial and financial systems, to eliminate the influence of COVID-19 on green credit’s suppression of solid waste, the data for the years 2020 and 2021, affected by the pandemic, will be excluded. The regression results can be found in Table 5 M1. As indicated by the results in M1, even after removing data from years impacted by the COVID-19 pandemic, it remains evident that green credit continues to significantly reduce solid waste generations, with the level of reduction even showing a slight enhancement. This further reinforces the accuracy of the conclusion. Therefore, it can be inferred that the COVID-19 pandemic had a broad and profound impact on the Chinese economy, resulting in disruptions and uncertainties in various economic activities. This unique circumstance could have influenced the heightened effectiveness of green credit in curbing solid waste generations

Table 5.

Robustness test.

SW	M1: exclude years affected by the COVID-19 pandemic			M2: exclude municipal data
SW	β	SE	Z	β	SE	Z
GL	−0.702***	0.097	−7.260	−0.435***	0.109	−4.000
FDI	−0.098*	0.053	−1.860	0.011	0.035	0.320
GDP	0.165	0.136	1.220	−0.287**	0.122	−2.350
IDD	−0.085	0.238	−0.360	0.229	0.213	1.080
FD	0.313	0.202	1.550	0.608**	0.191	3.190
CONS	3.516***	0.566	6.210	5.018***	0.510	9.840
Time control	Yes			Yes
Id control	Yes			Yes

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; IDD: industrial level.

p < 0.1. **p < 0.05. ***p < 0.01.

Exclude municipal data

To account for the higher economic development level and distinct characteristics in urban attributes, economic structure, financial outcomes and industrial composition in directly administered municipalities, data from these municipalities were excluded from the study sample. The regression results, as shown in Table 5 M2, reveal that even after removing data from directly administered municipalities, green credit continues to significantly reduce solid waste generations, albeit with a slightly less pronounced effect. This suggests that the presence of data from directly administered municipalities amplifies the impact of green credit on solid waste generations.

Heterogeneity

Considering the significant regional disparities in China, including geographical location, resource distribution, industrial infrastructure, financial structure and solid waste levels, the study aims to investigate the regional heterogeneity in the impact of provincial green credit on solid waste reduction. To address this heterogeneity, the research categorizes Chinese provinces into coastal and inland regions, as well as the Yangtze River Economic Belt and non-Yangtze River Economic Belt, based on China’s economic characteristics, as shown in Tables 6 and 7.

Table 6.

Heterogeneity 1.

SW	Yangtze River Economic Belt			Non-Yangtze River Economic Belt
SW	β	SE	Z	β	SE	Z
GL	−0.400***	0.120	−3.330	−0.630***	0.127	−4.970
FDI	−0.120**	0.053	−2.280	0.023	0.048	0.470
GDP	0.549***	0.152	3.610	0.116	0.212	0.550
IDD	0.762**	0.315	2.420	−0.094	0.284	−0.330
FD	0.028	0.175	0.160	0.805**	0.298	2.710
CONS	1.572**	0.590	2.660	3.139***	0.915	3.430
Time control	Yes			Yes
Id Control	Yes			Yes

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; IDD: industrial level.

p < 0.05. ***p < 0.01.

Table 7.

Heterogeneity.

SW	Coastal areas			Inland areas
SW	β	SE	Z	β	SE	Z
GL	−0.559***	0.118	−4.720	−0.695***	0.137	−5.080
FDI	0.072*	0.041	1.780	−0.035	0.063	−0.560
GDP	0.673***	0.193	3.490	−0.504**	0.163	−3.100
IDD	−2.066***	0.518	−3.990	0.359	0.250	1.430
FD	−0.031	0.238	−0.130	0.008	0.305	0.030
CONS	1.262	0.847	1.490	6.446***	0.664	9.710
Time control	Yes			Yes
Id control	Yes			Yes

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; IDD: industrial level.

p < 0.1. **p < 0.05. ***p < 0.01.

Differences in economic belts

Based on the data presented in Table 6, we can observe that green credit significantly mitigates solid waste generations in both the Yangtze River Economic Belt and non-Yangtze River Economic Belt regions. However, when considering the degree of mitigation, it becomes evident that green credit in non-Yangtze River Economic Belt regions has a more pronounced impact on solid waste reduction compared to the Yangtze River Economic Belt. The possible explanation for this observation lies in the fact that non-Yangtze River Economic Belt regions exhibit a lower level of industrialization and possess a relatively weaker environmental management system. Consequently, they face more severe environmental challenges compared to their Yangtze River Economic Belt counterparts. As a result, governments and businesses in these non-Yangtze River Economic Belt regions are more inclined to utilize green credit as a means to enhance solid waste management and improve resource utilization efficiency.

Regional disparities

From Table 7, it is evident that both in coastal and inland regions, green credit significantly reduces solid waste. However, in terms of the extent of reduction, green credit has a more pronounced effect on solid waste reduction in inland regions compared to coastal regions. The potential reason for this difference is that inland regions tend to focus more on heavy industries, which consequently generate more solid waste compared to coastal regions. With the national push for the development of waste-free cities, inland regions are facing increasing environmental governance pressure. This compels inland regions to continually refine their green credit systems, further intensifying their efforts to reduce solid waste.

This finding underscores the influence of regional disparities on the effectiveness of green credit in solid waste management. It also serves as a reminder to policymakers that policies related to green credit should be tailored to the unique characteristics and needs of different regions.

Threshold effect analysis

Building upon the theoretical analysis in the previous sections, this article selects green credit and environmental governance investment as threshold variables to examine the threshold values and threshold effects in the process of green credit’s role in mitigating solid waste. Subsequently, models are estimated and analysed.

Threshold variable: Green credit

Table 8 shows that the single threshold value for green credit passes the 5% significance test, whereas the double threshold value fails to pass the significance test. This indicates that there exists a single threshold value for green credit in the process of inhibiting solid waste, with a threshold value of 0.142. In addition, the article tests the consistency of the threshold estimation by constructing the likelihood ratio function and its 95% confidence interval. As shown in Figure 2, the construction process of the threshold estimation and confidence interval is evident. The LR statistic corresponding to the threshold estimation value of green credit is significantly smaller than the critical value. Therefore, it can be confirmed that the threshold estimation value is indeed valid and accurate.

Table 8.

Threshold value testing.

Threshold	Threshold	Fstat	Prob	Crit10	Crit5	Crit1
Single	0.142	35.470	0.014	22.889	27.338	36.553
Double	0.300	10.890	0.390	22.814	45.121	84.108

From Table 9, it can be observed that when green credit is below the threshold value of 0.142, it exhibits a suppressive effect on solid waste, but it does not pass the significance test. Only when green credit develops to a certain level, surpassing the threshold value of 0.142, does the suppressive effect of green credit gradually strengthen, increasing from 0.114 to 0.491, and passing the significance test. This indicates that green credit can indeed inhibit solid waste generations but needs to reach a certain level, thus validating research hypothesis 2.

Table 9.

Threshold regression analysis.

SW		β	SE	t	p > t	95% CI
FDI		−0.038	0.031	−1.220	0.223	−0.100 to 0.023
GDP		0.617 ***	0.057	10.900	0.000	0.506 to 0.728
FD		0.310 **	0.153	2.030	0.043	0.009 to 0.612
IDD		−0.070	0.188	−0.370	0.712	−0.440 to 0.301
GL <0.142	GL	−0.114	0.175	−0.650	0.516	−0.458 to 0.230
GL >0.142	GL	−0.491 ***	0.098	−5.010	0.000	−0.683 to −0.298

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; CI: confidence interval; IDD: industrial level.

p < 0.05. ***p < 0.01.

Threshold variable: Environmental governance investment

Table 10 provides evidence that there is a meaningful threshold value for Environmental Governance Investment when it comes to reducing solid waste generations. It is noteworthy that the single threshold value passes a 5% significance test, whereas the double threshold value does not meet the threshold for significance. This suggests that there is a single threshold effect for environmental governance investment in the context of green credit’s role in solid waste reduction, which is calculated to be 3.803.

Table 10.

Threshold value testing.

Threshold	Threshold	Fstat	Prob	Crit10	Crit5	Crit1
Single	3.803	28.730	0.020	16.788	22.314	42.688
Double	4.330	11.790	0.210	21.260	38.016	55.506

Additionally, the study assesses the reliability of this threshold estimation by constructing the likelihood ratio function and establishing a 95% confidence interval. Figure 3 visually outlines the process of determining the threshold value and its associated confidence interval. The LR statistic corresponding to the threshold estimation value for environmental governance investment is notably below the critical value. Consequently, we can confidently affirm the validity and precision of this threshold estimation

The findings in Table 11 reveal that when environmental governance investment surpasses the 3.803 threshold, the efficacy of green credit in solid waste reduction actually decreases. It drops from its initial level of 1.196–0.823, thereby confirming hypothesis 3. This phenomenon can be attributed to what we might call a ‘funding squeeze effect’. When environmental governance investment reaches a certain threshold, a substantial portion of available funds gets redirected towards environmental protection, including the construction and operation of pollution prevention facilities. This reallocation of funds can constrain the resources available for green credit, limiting its capacity to effectively kerb solid waste. Additionally, this situation might lead to skewed resource allocation. Excessive investment in environmental protection may limit resources for other areas, such as green innovation and industrial structural upgrades, potentially hindering their development and diminishing the overall impact of green credit.

Table 11.

Threshold regression analysis.

SW		β	SE	t	p > t	95% CI
FDI		−0.001	0.031	−0.040	0.970	−0.063 to 0.060
GDP		0.613	0.056	11.030	0.000	0.504 to 0.722
FD		0.175	0.152	1.150	0.251	−0.124 to 0.474
IDD		0.064	0.187	0.340	0.732	−0.304 to 0.433
EGI <3.803	GL	−1.196	0.138	−8.680	0.000	−1.466 to −0.925
EGI >3.803	GL	−0.823	0.119	−6.910	0.000	−1.057 to −0.589

EGI: environmental governance investment; GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; CI: confidence interval; IDD: industrial level.

In summary, a high level of environmental governance investment may potentially diminish the effectiveness of green credit in solid waste reduction due to factors like resource allocation, policy adjustments and funding constraints. Hence, it is crucial to consider these factors comprehensively when developing environmental policies and green credit strategies to strike a balance between environmental protection and economic growth.

Transmission mechanism analysis

To further elucidate the pathway through which green credit mitigates solid waste generations, this study introduces R&D investment intensity and industrial structure upgrading as intermediary variables. The focus is on the pathway of how green credit suppresses solid waste generations.

Transmission variable: R&D investment intensity

From Table 12 M1, it is evident that green credit significantly suppresses solid waste generations. Building upon M1, the inclusion of R&D investment intensity forms the regression results for M3. From M3, it can be observed that with the addition of the intermediary variable of R&D investment intensity, the inhibitory effect of Green credit on solid waste generations decreases from −0.689 to −0.549. Additionally, as indicated by M2, green credit strengthens a firm’s R&D investment intensity. Therefore, it can be inferred that R&D investment intensity serves as an intermediary bridge in the process through which green credit suppresses solid waste generations.

Table 12.

Transmission mechanism 2: R&D investment intensity.

Transmission mechanism	M1: GL → SW			M2: GL → RDI			M3: GL → RDI → SW
Transmission mechanism	β	SE	Z	β	SE	Z	β	SE	Z
GL	−0.689***	0.095	−7.220	0.912***	0.175	5.220	−0.549***	0.095	−5.800
RDI							−0.153***	0.026	−5.850
FDI	−0.003	0.037	−0.070	−0.149	0.068	−2.210	−0.025	0.036	−0.710
GDP	−0.010	0.120	−0.080	−0.087	0.220	−0.400	−0.024	0.115	−0.200
IDD	0.053	0.220	0.240	0.103	0.404	0.260	0.069	0.212	0.330
FD	0.506**	0.193	2.620	−0.586	0.354	−1.650	0.416**	0.186	2.230
_cons	3.923***	0.511	7.680	2.269**	0.936	2.430	4.271***	0.494	8.640
Time control	Yes			Yes			Yes
Id control	Yes			Yes			Yes

R&D: research and development; RDI: R&D investment intensity; GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; IDD: industrial level.

p < 0.05. ***p < 0.01.

Transmission variable: Upgrading of industrial structure

To scientifically validate the mediating effect of industrial structure upgrading in the process of green credit suppressing solid waste generations, the study subdivides industrial structure upgrading into industrial structure rationalization and industrial structure advancement. This subdivision aims to enhance the understanding of the transmission effects of industrial structure upgrading.

Firstly, from Table 13 M1, it can be observed that green credit significantly suppresses solid waste generations. Building upon M1, the inclusion of industrial structure rationalization forms the regression results for M3. From M3, it is evident that with the addition of the intermediary variable of industrial structure rationalization, the inhibitory effect of green credit on solid waste generations decreases from −0.689 to −0.602. Additionally, as indicated by M2, green credit promotes industrial structure rationalization. Therefore, it can be inferred that industrial structure rationalization serves as an intermediary bridge in the process through which green credit suppresses solid waste generations. Similarly, the study verifies that industrial structure advancement serves as an intermediary bridge in the process through which green credit suppresses solid waste generations. Thus, the hypothesis 5 is confirmed.

Table 13.

Transmission mechanism 2: industrial structure upgrading.

Transmission mechanism	M1: GL → SW			M2: GL → ISR			M3: GL → ISR → SW
Transmission mechanism	β	SE	Z	β	SE	Z	β	SE	Z
Transmission mechanism: industrial structure rationalization
GL	−0.689***	0.095	−7.220	0.582***	0.104	5.620	−0.602***	0.098	−6.150
ISR							−0.150***	0.045	−3.290
FDI	−0.003	0.037	−0.070	0.055	0.040	1.370	0.006	0.037	0.150
GDP	−0.010	0.120	−0.080	−0.703***	0.130	−5.400	−0.115	0.123	−0.940
IDD	0.053	0.220	0.240	0.604**	0.239	2.520	0.143	0.220	0.650
FD	0.506**	0.193	2.620	0.223	0.210	1.060	0.539**	0.191	2.820
_cons	3.923***	0.511	7.680	2.729***	0.554	4.920	4.332***	0.520	8.340
	M4: GL → SW			M5: GL → ISA			M6: GL → ISA → SW
	β	SE	Z	β	SE	Z	β	SE	Z
Transmission mechanism: industrial structure advancement
GL	−0.689***	0.095	−7.220	0.630***	0.110	5.750	−0.539***	0.096	−5.640
ISA							−0.238***	0.042	−5.680
FDI	−0.003	0.037	−0.070	0.227***	0.042	5.370	0.052	0.037	1.400
GDP	−0.010	0.120	−0.080	−0.702***	0.138	−5.100	−0.177	0.119	−1.490
IDD	0.053	0.220	0.240	−3.382***	0.253	−13.360	−0.751**	0.255	−2.940
FD	0.506**	0.193	2.620	0.451**	0.222	2.030	0.613***	0.187	3.280
_cons	3.923***	0.511	7.680	4.529***	0.587	7.720	5.000***	0.527	9.480
Time control	Yes			Yes			Yes
Id control	Yes			Yes			Yes

GDP: gross domestic product; FD: financial decentralization; FDI: foreign direct investment; SW: solid waste; GL: green credit; SE: standard error; IDD: industrial level; ISA: industrial structure advancement; ISR: industrial structure rationalization.

p < 0.05. ***p < 0.01.

Comparing Table 13, it is evident that the mediating effects of industrial structure rationalization and industrial structure advancement have been confirmed. However, when considering the mediating effects, industrial structure advancement has a greater impact than industrial structure rationalization. The possible reason for this is that industrial structure advancement, relative to industrial structure rationalization, utilizes green credit to improve the environmental performance of existing industries and promote the emergence of new environmentally friendly industries. This makes the overall economy more environmentally sustainable, resulting in more significant benefits in solid waste management.

Conclusions

Research conclusions

The reduction of solid waste generations stands as a robust cornerstone in the development of waste-free cities and the pursuit of high-quality economic and societal progress. Building upon this premise, this article delves into the concept of ‘waste-free cities’ and systematically elucidates the intricate relationship between green credit and solid waste within a comprehensive theoretical framework. Leveraging sophisticated analytical tools, including panel threshold models, and mediation effect models, and drawing upon provincial panel data from China, the study rigorously examines the mechanisms through which green credit exerts its influence on solid waste. The research findings shed light on the following critical insights:

Green credit has significantly reduced the emissions of solid waste, though regional differences exist: The study demonstrates that green credit significantly reduces solid waste generations, further substantiating the effectiveness of green finance in environmental pollution control (Li et al., 2023b; Lin and Pan, 2023; Wang et al., 2020; Zhang et al., 2023). Differing from existing literature, this research narrows its focus to the aspect of green credit. To ensure the robustness of its conclusions, it specifically selects data from years marked by key events for analysis, thereby minimizing the potential impact of extraordinary events on the scientific validity of its findings. Furthermore, in examining regional heterogeneity, the text considers the role of regional economies in the pathway through which green credit affects solid waste. It divides China into the Yangtze River Delta Economic Belt and areas outside this belt for analysis, deviating from the conventional geographic regional divisions commonly used in academic research (Guo and Tan 2024; Smith et al., 2018). This approach enriches the perspective on heterogeneity research by introducing a new dimension of differentiation.

Green credit significantly correlates with the suppression of solid waste generations, a relationship that is intricately linked to the maturity of the green credit system and the extent of government environmental investments. It is the combination of moderate green credit and government environmental investment that can accelerate the reduction of solid waste generations. This finding resonates with the research conducted by Guo and Tan (2023). However, this study expands the exploration of the threshold effect by considering both internal and external factors, reevaluating the marginal conditions under which green credit impacts solid waste generations. By introducing a new perspective on balancing the credit system with government support efforts, this research enriches the theoretical framework of existing literature, offering a novel viewpoint on the interplay between green credit and environmental policy efficacy.

R&D investment and industrial structure upgrading act as intermediary bridges in the process of green credit mitigating solid waste generations. This conclusion aligns with the findings of Guo and Tan (2023), Li et al. (2022a) and Wang et al. (2022), suggesting that the financial system’s role in curbing environmental pollution requires the transmission through intermediary variables. This study further refines the connection between green finance and environmental pollution by introducing R&D investment and industrial restructuring. It clarifies the transmission mechanism of green credit in the process of suppressing solid waste generations, revealing the intricate interactions between financial instruments and solid waste. This enriches the research on green finance at the regional environmental level, offering a new perspective for green finance to promote environmental governance.

Research limitations

Firstly, the study employed provincial panel data from China, which, despite covering a wide geographical range, may not encompass all global scenarios. Thus, the regional and general applicability of the study might be somewhat constrained.

Secondly, the research primarily focused on the impact of green credit on solid waste generations without delving into other potential factors that could affect solid waste management. Future research could consider including more diverse factors such as sociocultural elements, policy regulations and market demand to gain a comprehensive understanding of the complexity of solid waste management.

Additionally, although the study accounted for research and development intensity and industrial structure upgrading when analysing mediation effects, the influence of these intermediary factors still requires further investigation to unveil more details and mechanisms.

Policy recommendations

Strengthen the formulation and implementation of green credit policies

Given the crucial role of green credit in reducing solid waste generations, the government should enact more specific and mandatory green credit policies. These policies should include minimum requirements for banks and other financial institutions in investing in green projects, while also offering incentives such as tax breaks and preferential loan rates to encourage financial institutions to increase investments in environmental projects.

Promote R&D and technological innovation

The government should incentivize companies to increase their investment in environmental technology R&D through fiscal subsidies and tax incentives. Additionally, establishing collaboration platforms between enterprises, universities and research institutions should be prioritized to accelerate the development and application of environmental technologies.

Support the adjustment and upgrading of industrial structures

Policies should be developed to encourage industries to transition towards green, low-carbon and circular economies. By providing financial support and technical guidance, businesses can improve their production processes, enhance resource efficiency and reduce solid waste generation.

Improve the transmission mechanism of green credit

A comprehensive mechanism should be established to ensure that green credit effectively promotes environmental technology R&D, as well as the adjustment and upgrading of industrial structures. This includes enhancing communication between financial institutions and environmental departments, establishing evaluation and approval processes for green projects and monitoring and assessing the implementation effects of green credit projects.

Intensify the regulation and support of environmental policies

The enforcement of environmental regulations should be strengthened, with penalties imposed on enterprises that violate environmental laws. At the same time, support and incentives should be provided to businesses that implement environmental measures. By creating a fair competitive market environment, enterprises will be more actively involved in environmental governance.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: National Social Science Fund of China (24BGL207); Jiangsu Provincial Philosophy and Social Science Foundation (23GLB019).

Notes

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