European economies must prioritize sustainable growth in their development goals by 2030. These countries’ aggressive economic progress necessitates considerable demands for energy and raw resources, posing environmental concerns like resource exploitation and garbage creation. Despite their recent great economic success, its environmental effect has been alarming. Circular economy strategies can increase resource efficiency by collecting and reusing trash in manufacturing processes, reducing waste creation. However, adopting a circular economy strategy also needs careful thought while deciding on pollution control technology in one sector to avoid the transmission of emissions and environmental concerns to business sectors. This study aims to examine the link between financial policies relating to climate change and circularity performance.
The authors use a panel-corrected standard errors (PCSE) model, a feasible generalized least square model and the two-step general method of moment to explore this link. Furthermore, the results from the dynamic fixed effects in the autoregressive distributed lag method (DFE-ARDL) suggest the short-term and long-term relationships.
The findings illustrate the heterogeneous influences of climate-related financial policies on the different issues of circularity performance. Furthermore, the results from the DFE-ARDL suggest that the impact of financial development only becomes apparent in the long term. The findings suggest that it is crucial to monitor the effects of financial policies on the transition toward the circular economy to develop better strategies and policies.
The findings provide essential policy suggestions to help European countries design financial policies to pursue circular economies. Transitioning to a circular economy will undoubtedly present challenges and difficulties, with trade protectionism being one of the potential obstacles. European countries’ governments and authorities should actively encourage and enhance their capabilities to capitalize on opportunities created by the implementation of these climate-related financial policies. Among all approaches and tools to support nations in achieving the goal, the authors strongly recommend prioritizing the improvements of climate-related financial policy performance. The findings in the European region also suggest an insightful lesson for selecting an appropriate tool to facilitate other countries’ transition toward sustainable development and contributing to a greener world.
This study examines the link between financial policies relating to climate change and circularity performance. In this paper, the authors contribute to the research in three ways. First, to the best of the authors’ knowledge, it is the first empirical study to explore climate-related financial policies as a contributor to the implementation of circularity. This research contributes to the existing body of information by investigating the climate-related financial policies’ influence on circularity performance as assessed by various dimensions. Various measures are used to gauge four dimensions of circularity performance in this paper. Economists and policymakers can use this index to develop and implement environmental policies by capturing multidimensional circularity performance issues. Second, this paper uses a PCSE model based on cross-sectional dependence and stationarity tests. The study focuses on countries in a European Union region, which contribute significantly to global carbon emissions and represent a varied spectrum of rich and emerging economies.
1. Introduction
The European Union (EU) action plan for the circular economy (CE) was released in December 2015, coinciding with a new global climate change treaty agreement at COP21 in Paris (Behrens, 2016). While the timing may be coincidental, it emphasizes the interdependence between natural resource consumption and climate change. Global resource extraction was roughly 73 billion tonnes in 2010, with global greenhouse gas (GHG) emissions totaling around 50 billion tonnes. Furthermore, over 10 billion tonnes of municipal and industrial waste were created globally. In the report by Behrens (2016), more than 80% of yearly raw material inputs were returned to the environment, pollution and garbage. The remainder mainly went on construction and infrastructure development. These data highlight the importance of emissions in physical production in the global economy. Indeed, in 2023, GHG emissions accounted for more than 80% of material outflows, making the atmosphere the primary destination for global trash disposal (European Commission, 2024a, 2024b).
In 2020, the new circular economy action plan was adopted, which is the key block of the European Green Deal. As the EU transitions to a CE, pressure on natural resources will be reduced, and jobs and sustainable growth will be created. It is also a prerequisite for achieving the EU’s 2050 climate neutrality target and for halting the loss of biodiversity. According to the new action plan, initiatives will be implemented across the entire product life cycle. In addition to addressing how products are designed, promoting CE processes and encouraging sustainable consumption, the program also aims to prevent waste and maintain resources for as long as possible within the EU economy. The amount of raw materials used in industrial operations, the energy required and the resulting GHG emissions all have a direct physical link. GHG emissions occur throughout the life cycle of a product, including extraction, manufacture, consumption and waste disposal. For example, raw materials manufacturing accounts for roughly 19% of worldwide GHG emissions, with the waste sector accounting for an additional 3% (European Commission, 2024a, 2024b). To minimize global warming GHG emissions by at least 60% by 2050, a transition to low-carbon and renewable energy sources alone will not be sufficient, as stated in Article 2 of the Paris Agreement. Climate policy for better resource efficiency is required in a CE, with higher reuse and recycling and reduced raw material use.
Both developing and advanced economies must prioritize sustainable growth in their development goals by 2030 (Sawhney, 2021). The country’s aggressive economic progress necessitates considerable demands for energy and raw resources, posing environmental concerns like resource exploitation and garbage creation (Agrawal et al., 2023; Bjelić et al., 2024; Causevic et al., 2022; Khan et al., 2021a, 2021b; Zakari and Khan, 2021). Despite their recent great economic success, its environmental effect has been alarming. According to the 2018 Environmental Performance Index, many developing countries have a low ranking, which was primarily attributed to deteriorating ambient ecological quality as a result of factors such as poor solid waste disposal, increasing particulate pollution, chemical fertilizers and insecticides used excessively, sewage and untreated liquid waste poured into rivers and an increase in hazardous electronic waste and nonbiodegradable garbage. As a result of the consequent degradation of air, water and soil quality, severe human health concerns, including disease and mortality, have arisen. Urgent action is required to combat reducing pollution in manufacturing, enhancing resource efficiency and reducing consumption (Sawhney, 2021). Decoupling economic development sewage and untreated liquid waste poured into rivers.
CE strategies can increase resource efficiency by collecting and reusing trash in manufacturing processes, reducing waste creation (Bjelić et al., 2024; de Jesus et al., 2018; Hong Nham and Ha, 2022; Jesic et al., 2021; Radovanović et al., 2017). Adopting a CE strategy also needs careful thought while deciding on pollution control technology in one sector to avoid the transmission of emissions and environmental concerns to business sectors. It is critical to prevent generating new environmental and societal issues when attempting to reduce pollution (Aranda-Usón et al., 2019). With the introduction of a Circular Economy bundle by the EU in 2015 and the implementation of the Circular Economy Law of China in 2008, the government’s policy think tank among developing and advanced countries saw the need to build a framework that encourages resource efficiency across all sectors. Specifically, the critical issue in developing countries is a firm reliance on native raw materials and imported petroleum, accounting for nearly 97% of all materials used in economic activity (Ha et al., 2023; Sawhney, 2021). They initially concentrated on the aluminum, steel, construction/demolition and e-waste sectors when advocating ways to promote the CE model. While the energy sector, along with other critical industries, was considered to have sufficient natural resource usage methods, there has been a longstanding to secure countries’ energy security.
Indeed, many developing countries confront a vast problem with energy poverty because it is home to a sizable part of the world’s poor. Particularly, India is home to around 30% of the world’s impoverished, 24% of the world’s people who do not have access to power and 30% of the people who cook using solid biomass (Sawhney, 2021). Vietnam and other developing countries experienced a similar issue (Agarwala, 2021; Ha et al., 2023). As stipulated in Sustainable Development Goal 7, by 2030, ensuring that the whole population has access to innovative energy services is a big problem for the country. These countries want to lower total emission intensity while also improving energy efficiency. They have deliberately established an institutional framework for supporting sustainable energy during the past two decades.
Despite efforts over the past two decades to create a robust renewable energy institutional structure, there has been a considerable omission of the possible environmental damage linked with renewable energy technology to minimize GHG emissions. The CE idea, which attempts to reduce waste and enhance resource efficiency, has been largely ignored in the renewable energy sector. To reduce GHG emissions, there has been an effective concealment concerning the potential environmental damage associated with renewable energy technologies. A thorough and efficient solution would require ensuring the growth of renewable energy sources is still ongoing (Gielen et al., 2019; Lv, 2023).
This study examines the link between financial policies relating to climate change and circularity performance. This article contributes to the research literature in three ways. Firstly, it is the first empirical study to explore climate-related financial policies as a contributor to the implementation of circularity. This research contributes to the existing body of information by investigating the influence of climate-related financial policies on circularity performance, as assessed by various dimensions. In this article, various measures are used to gauge four dimensions of circularity performance. Economists and policymakers can use this index to develop and implement environmental policies by capturing multidimensional circularity performance issues. Secondly, this paper uses a panel-corrected standard error (PCSE) model based on cross-sectional dependence and stationarity tests. Furthermore, the findings can be further verified using feasible generalized least square (FGLSs) and two-step general method of moment (GMMs) considering heteroscedasticity and endogeneity. Long-term and short-term impacts of autoregressive distributed lag (ARDL) methods were also investigated using pooled mean groups (PMG). PMG-ARDL could be applied to both country- and time-fixed effects, according to Ha and Thanh (2022) and Thanh et al. (2022b). An integrated and dynamic business model and public policy for circularity performance will support the dynamics of the transformation, which will require a broader scope of research. The study focuses on countries in an EU region, which contribute significantly to global carbon emissions and represent a varied spectrum of rich and emerging economies. As a result, it is essential to investigate EU perspectives on climate-related finance policy.
The following is the way the paper is structured. Section 2 explains the study by summarizing an overview of the relevant literature. Section 3 discusses the study’s data sources and the research methods used. The following section presents and examines the study’s findings. Finally, Section 5 provides final thoughts and investigates the research’s policy implications.
2. Literature review
2.1 A country’s path toward the circular economy
The first global reaction to climate change and GHG stabilization concentrations happened in 1992, on the occasion of the Rio de Janeiro Conference on Environment and Development. This resulted in the creation of the United Nations Framework Convention on Climate Change (UNFCCC), which went into force in 1994. The UNFCCC established a discussion forum among its participants, and the Kyoto Protocol was approved in 1997. The Kyoto Protocol, which was enacted in 2005, mandated that Annex I nations agree to specified carbon reduction targets by 2012. The Paris Agreement was signed in 2015 and went into force in 2016. The goal of the accord is to maintain global warming in the 21st century to less than 2°C over preindustrial levels (Agrawal et al., 2023; Jesic et al., 2021; Radovanović et al., 2022; Ramčilović Jesih et al., 2024).
There has been a global trend of lowering energy intensity in recent years, with an average yearly decline of 2.1% since 2010. Furthermore, carbon emissions from energy usage leveled out between 2014 and 2016. This encouraging trend can be linked to the growing use of renewable energy sources and efficiency improvements. The renewable energy sector has seen considerable technical advancements and cost savings, primarily due to greater competition and supporting laws in many nations. Considering the global objective of achieving the goal that by 2030, its population will have access to inexpensive, dependable and advanced energy services, increasing the contribution of renewable energy to the entire energy mix is critical. This phase is crucial for mitigating environmental deterioration and meeting rising energy demand.
In general, renewable energy is used to generate electricity that emits 5%–6% of GHG emissions from coal-fired power plants and 8%–10% of those produced from gas-fired power plants (Amin et al., 2022; Sompolska-Rzechuła et al., 2024). Significant expenditures are required to reduce global warming and investments in low-carbon electrical technology development and deployment to 2°C. This shift demands broad usage of nuclear power, renewable energy and fossil fuel power plants outfitted with carbon capture and sequestration, which are examples of clean coal technology. Alternatives to conventional fossil fuels would not only cut GHG emissions but also address other air pollution concerns, such as particulate particles and acid rain, which are also problems.
Integrating a model of the CE into the development of electricity generation would include assessing environmental and resource issues and consequences of various energy technologies throughout their entire life cycle, from manufacture to disposal (de Souza Andrade et al., 2024; Evans, 2023; Reindl et al., 2024; Seljak et al., 2023). This complete evaluation will enable informed decision-making that tackles climate change, environmental concerns and health risks connected with fossil fuels, all while addressing rising global energy demands driven by population increase. The best technology for each country would differ based on their unique conditions. Compared to the Kyoto Protocol, the Paris Agreement takes a more flexible approach, allowing nations to develop nationally determined contributions and proactive plans of action at the national level to reduce global warming to 2°C jointly.
2.2 Raising the efficiency of climate policy on circular economy
The EU action plan for the CE released in 2015 is less ambitious. If well implemented, it has the potential to reduce GHG emissions by 500 million tonnes in the EU between 2015 and 2035 (Behrens, 2016). This decrease represents roughly 10% of total EU GHG emissions currently. The action plan explicitly addresses waste management through legislative recommendations that create standard EU objectives rates of municipal and packaging waste recycling (65% and 75%, respectively, by 2030) (European Commission, 2024a, 2024b). It also contains a legally enforceable aim of limiting landfill to no more than 10% of total waste by 2030. Furthermore, the action plan presents novel ecodesign suggestions beyond energy efficiency to include reparability, upgradability, durability, the identification of particular materials and recyclability (Barkhausen et al., 2022; de Souza Andrade et al., 2024; Reindl et al., 2024).
While the targets and proposed measures in the Circular Economy Action Plan in 2015 were expected to reduce both inputs (natural resources) and outputs (emissions and waste) in the EU economy, there is a lack of express high-level political commitment to reducing total EU resource consumption within the context of the CE. Therefore, the EU’s new circular action plan was released in 2020. By implementing the circular action plan, the EU aims to create a more competitive and cleaner Europe. By transitioning to a CE, the EU will reduce its pressure on natural resources and create jobs and sustainable economic growth. It is one of the main building blocks of the European Green Deal, Europe’s new agenda for sustainable growth. In addition, it is imperative to achieve the EU’s 2050 climate neutrality target and to prevent biodiversity loss. A new action plan outlines a series of initiatives that will be implemented throughout the life cycle of the product. This program not only promotes CE processes and encourages sustainable consumption but also prevents waste and ensures that resources are maintained within the EU economy for as long as possible.
The new framework supports the EU’s circular economy and climate neutrality ambitions under the European Green Deal. For example, an amendment to the Packaging and Packaging Waste Directive was proposed by the Commission on November 30, 2022 (European Commission, 2024a, 2024b). This report contributes to achieving the objective of the European Green Deal and the new circular economy action plan of reusing or recycling all packaging on the EU market economically by 2030. By doing so, it will contribute to the objective of the 2018 Plastics Strategy to ensure that all plastic packaging placed on the market by 2030 can be reused or recycled in a cost-effective manner. A proposal for a Directive on Green Claims was adopted by the Commission in March 2023. This proposal complements and further operationalizes the proposal for a Directive on empowering consumers in the transition to a green economy. On July 12, 2023, the European Parliament and the Council adopted the new Batteries Regulation. As a result, the environmental impact of this exponential growth will be minimized in consideration of changing socioeconomic conditions, technological developments, market conditions and battery usage patterns. It is an important achievement under the European Green Deal, as it brings forward the CE and zero pollution goals of the EU and strengthens the EU’s strategic autonomy. The European Green Deal includes a proposal for companies to demonstrate that they are substantiating their environmental claims with robust, science-based and verifiable methods as part of the Circular Economy Action Plan. All of these initiatives are aimed at establishing a coherent framework for promoting sustainable goods, services and business models in the EU, as well as transforming consumption patterns in a more sustainable direction. The goals of these initiatives are to reduce the environmental footprint of EU products significantly and to contribute to the EU’s goal of becoming climate neutral by the year 2050 (Jesic et al., 2021; Muth et al., 2024; Radovanović et al., 2022; Ramčilović Jesih et al., 2024).
An integral component of the EU’s energy transition is the decarbonization of the European economy, which aims to become a leader in this process by 2050 and to include other European countries, making the European continent the first carbon-neutral region in the world (Jesic et al., 2021; Muth et al., 2024; Radovanović et al., 2022; Ramčilović Jesih et al., 2024). Energy efficiency and pollution related to energy are significant obstacles to the green transition and the region’s transition to a CE and carbon neutrality. Implementing circularity and carbon neutrality as a long-term objective of the EU is not necessarily related to economic development, nor can its trajectory be determined exclusively through data processing and monitoring by incorporating qualitative data to a greater extent into the analysis of a carbon-neutral future (Ramčilović Jesih et al., 2024), a more precise understanding of a carbon-neutral future can be achieved. Although decarbonization is an important objective of the EU, there is currently no clear model for monitoring the process, and there is a disagreement regarding the social, economic and security implications associated with prioritizing decarbonization. Furthermore, the financial investments required to achieve these goals are insufficient. In the Recovery Plan for Europe, 30% of the huge budget will be invested in climate change, with the aim of achieving zero GHG emissions by 2050 (Radovanović et al., 2022). It will be necessary to invest in research and innovation to achieve this ambitious plan. Our objective is to contribute to an effective long-term investment policy, mitigate the effects of climate change and contribute to the mitigation of these effects (Jesic et al., 2021). In the context of the European region, it is therefore essential to investigate a nexus between climate-related financial policies and circularity performance.
3. Method
The model used to investigate the nexus of climate-related financial policies (CP) and circularity performance (CE) can be presented as follows:
where i and t, respectively, represent country i and year t. and are added into the model to capture the country and year-fixed effects, and is the error term.
3.1 Circularity performance
As in Di Maio et al. (2017), Hong Nham and Ha (2022) and Sawhney (2021), this study uses six diverse measures to capture the circularity performance of European countries, including municipal waste (CE_MW) measured as a generation of municipal waste per capita (kilograms per capita); the number of patents associated with recycling and secondary raw materials (CE_PA); circular material usage (CE_MA) measured as the circular material use rate (%); recycling waste performance (CE_RW) measured as the recycling rate of all waste excluding major mineral waste (%); recycling biowaste performance (CE_RB) measured as the recycling rate of biowaste; and recycling e-waste performance (CE_RE) measured as the recycling rate of e-waste (%). These variables are taken from the European Statistics (Eurostat) from 2012 to 2020. The study decided to use databases from countries in the European region since this region provides an updated database that fully captures various dimensions of circularity performance. Due to the special properties of the panel database, adequate empirical analysis requires no gap in the sample. After dropping any countries with missing observations, the final sample consists of 17 countries from 2012 to 2020.
3.2 Key explanatory variable
The key explanatory variable is a stock of climate-related financial policies (CP). This variable illustrates a number of climate-related financial policies per year implemented by countries during the 2012–2020 period. This variable is sourced from D’Orazio (2021), D’Orazio and Löwenstein (2022), D’Orazio and Thole (2022) and D’Orazio and Valente (2019).
3.3 Control variables
Literature-based empirical studies, especially (Bu et al., 2019; Hong Nham and Ha, 2022; Shahbaz et al., 2018; Sun et al., 2019), were used to determine the control variables. Research based on five variables is as follows. The article took economic growth (EG), trade share (TS) and industrialization level (IND) as explanatory variables. This paper also adds the proportion of net FDI inflows (FDI) in the theoretical model, as in Bu et al. (2019), Shahbaz et al. (2018) and Sun et al. (2017, 2019). These variables are available from World Development Indicators (WDI). Besides, the democratization level (GE) included in the model was taken from finnish social science data archive (FSSDA). The final sample consists of cleaning data from 17 countries during the 2012–2020 period.
Table 1 provides the information (including measures and data sources) and statistical description (including the number of observations [Obs], mean [Mean], standard deviation [SD] and minimum [Min] and maximum [Max] value) of all included variables.
Description of variables
| Variable | Definition | Measure | Source | Obs | Mean | SD | Min. | Max. |
|---|---|---|---|---|---|---|---|---|
| CP | Climate-related financial policies | Countries implement a number of climate-related financial policies per year | (D’Orazio, 2021) | 153 | 494.13 | 125.41 | 256.00 | 830.00 |
| CE_MW | Per capita municipal waste | Generation of municipal waste per capita (kilograms per capita) | Eurostat [45] | 144 | 7.92 | 8.76 | 0.00 | 45.01 |
| CE_PA | Related patent number | Patents related to recycling and secondary raw materials | Eurostat [46] | 126 | 4337.98 | 4988.28 | 198.60 | 19457.40 |
| CE_MA | Circular material usage | Share of circular material use (%) | Eurostat [47] | 144 | 10.45 | 7.11 | 1.20 | 30.90 |
| CE_RW | Recycling waste | The percentage of waste that is recycled, excluding major mineral wastes (%) | Eurostat [48] | 153 | 33.68 | 16.99 | 0.00 | 64.30 |
| CE_RB | Recycling biowaste | Per capita recycling of biowaste (kilograms) | Eurostat [49] | 153 | 69.72 | 58.25 | 0.00 | 231.00 |
| CE_RE | Recycling e-waste | E-waste recycling rate (%) | Eurostat [50] | 142 | 42.47 | 11.74 | 10.50 | 68.80 |
| EG | Economic growth | The real GDP per capita (constant 2010 US dollars) | WDI [51] | 153 | 15.86 | 14.16 | 0.00 | 66.67 |
| TS | Trade share | The proportion of GDP | WDI [52] | 153 | 40.10 | 27.50 | 3.77 | 111.15 |
| FDI | Net inflow of foreign direct investment | The proportion of GDP | WDI [53] | 153 | 1.25 | 0.72 | 0.55 | 4.08 |
| IND | Industrialization level | The value added to GDP | WDI [54] | 153 | 0.04 | 0.28 | −1.54 | 1.63 |
| GE | Level of democratization | The index of democratization | FSSDA [55] | 153 | 0.23 | 0.06 | 0.11 | 0.37 |
Source(s): Authors’ own creation
Table 1 summarizes the key information of the included variables. There are 153 observations in our sample. These variables’ data are distributed normally around their mean values.
Table 2 lists the countries in our sample. Our unique database includes 17 European countries.
Countries in the sample
| EU countries | |
|---|---|
| Austria | Iceland |
| Belgium | Italy |
| Bulgaria | Lithuania |
| Czech Republic | Luxembourg |
| Denmark | The Netherlands |
| Spain | Poland |
| Estonia | Sweden |
| United Kingdom | |
| Croatia | |
| Hungary | |
Source(s): Authors’ own creation
Table 3 reports the correlation matrix between all included variables.
Correlation coefficients
| CE_MW | CE_PA | CE_MA | CE_RW | CE_RB | CE_RE | CP | EG | TS | FDI | IND | GE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CE_MW | 1 | |||||||||||
| CE_PA | 0.0681 | 1 | ||||||||||
| CE_MA | 0.331*** | 0.715*** | 1 | |||||||||
| CE_RW | 0.484*** | 0.156 | 0.248* | 1 | ||||||||
| CE_RB | 0.583*** | 0.186 | 0.341*** | 0.863*** | 1 | |||||||
| CE_RE | 0.507*** | −0.150 | −0.161 | 0.212* | 0.101 | 1 | ||||||
| CP | 0.407*** | 0.156 | 0.314** | 0.459*** | 0.418*** | 0.00317 | 1 | |||||
| EG | 0.559*** | 0.146 | 0.321*** | 0.719*** | 0.711*** | 0.158 | 0.436*** | 1 | ||||
| TS | −0.106 | −0.261** | −0.408*** | 0.0855 | 0.0153 | 0.0125 | −0.175 | −0.176 | 1 | |||
| FDI | 0.306** | 0.108 | 0.255** | 0.363*** | 0.423*** | −0.0398 | 0.333*** | 0.417*** | 0.210* | 1 | ||
| IND | −0.256** | 0.145 | −0.0673 | −0.125 | −0.0830 | 0.0298 | −0.406*** | −0.357*** | 0.202* | −0.295** | 1 | |
| GE | 0.406*** | 0.171 | 0.314** | 0.698*** | 0.709*** | 0.0985 | 0.451*** | 0.481*** | −0.0598 | 0.429*** | −0.121 | 1 |
Note(s): *p < 0.05; **p < 0.01 and ***p < 0.001
Table 3 suggests that the correlations between explanatory variables are below 0.8, which implies that there may not exist an issue of multicollinearity in our developed model. Moreover, this table suggests a positive association between climate-related financial policies (CP) and circularity performance (CE).
The following steps are followed in the conduct of a cross-sectional dependence (CD) test. This paper validates the presence of CD in the data by using the cross-sectional dependence tests proposed by Pesaran (2021). The next step will be to examine the stationarity of the included variables with the presence of CD using unit root tests developed by Im et al. (2003). Table 4 reports the results.
Cross-sectional dependence tests and stationary tests
| Variable (in level) | CD test, Pesaran (2004) | Im−Pesaran−Shin test (Z-bar) | Variable (in difference) | Im−Pesaran−Shin test (Z-bar) |
|---|---|---|---|---|
| CE_MW | 7.712*** | 4.135 | DCE_MW | −2.524*** |
| CE_PA | 4.561*** | −0.453 | DCE_PA | −4.251*** |
| CE_MA | 0.56 | 5.055 | DCE_MA | −4.224*** |
| CE_RW | 19.481*** | −0.376 | DCE_RW | −5.212*** |
| CE_RB | 0.184 | −0.068 | DCE_RB | −5.050*** |
| CE_RE | 8.126*** | 0.871 | DCE_RE | −3.483*** |
| CP | 5.672*** | −0.237 | DCP | −4.722*** |
| EG | 20.739*** | −1.559** | DEG | −5.225*** |
| TS | 42.070*** | 3.007 | DTS | −3.698*** |
| FDI | 14.973*** | 0.463 | DFDI | −3.241*** |
| IND | 0.103 | −4.056*** | DIND | −4.653*** |
| GE | 0.034 | 9.771 | DDM | −3.370*** |
Note(s): ***, **, and * represent 1, 5, and 10% significance level, respectively
According to Table 4, some variables do not become stationary after the first level of difference. However, others become stationary at the second level of difference. To investigate the financial policies-circularity nexus for the data with CD and stationarity of the first-difference variables, the PCSE model is the most appropriate empirical approach (Beck and Katz, 1995; Ha, 2022b, 2022a, 2022c; Le et al., 2022). PCSE modeling was used to investigate the relationship between climate policy and circularity performance. Traditional methods, such as fixed-effect or random-effect models, are inappropriate for the dynamic panel with CD, where the time is short (T is short) and the number of entities is small (N is small), as Pesaran (2021) argues. The outcomes of these methods will be skewed (Balsalobre-Lorente et al., 2022; Nguyen and Su, 2021). According to equations (1) and (2), all the explanatory variables are lagged by one period. This minimizes the possibility of endogeneity caused by the simultaneous relationship between climate-related financial policies and circularity. In addition, this article replicated the estimates using different statistical models, including the FGLS to address the issue of heterogeneity (Ha, 2022b) and the two-step system GMM to deal with the potential issue of heterogeneity in equations (1) and (2) (Ha and Thanh, 2022; Sweet and Eterovic Maggio, 2015; Thanh et al., 2022a).
Notably, the effects of the project over the short- and long term are also assessed. Accordingly, the ARDL method developed by Pesaran and Smith (1995) is used. In this model, a dynamic fixed-effects estimate (DFE) is used to account for the causal relationship between variables and the heteroscedasticity across countries due to the potential presence of endogeneity (Pesaran and Shin, 1998). The first step in the estimation process is to test for cointegration between the two variables by using the Kao, Pedroni and Westerlund cointegration test, which was, in turn, developed by Kao (1999), Pedroni (2004) and Westerlund (2005). Table 5 outlines these findings.
Cointegration test
| Kao test | Pedroni test | |
|---|---|---|
| Model: f(CP and circularity) | Dickey−Fuller test | Phillips−Perron test |
| CP | ||
| CE_MW | −2.33*** | −23.52*** |
| CE_PA | −3.28*** | −18.88*** |
| CE_MA | −4.52*** | −11.86*** |
| CE_RW | −4.38*** | −22.95*** |
| CE_RB | −4.23*** | −17.95*** |
| CE_RE | −3.47*** | −18.93*** |
Note(s): ***, **, and * represent 1, 5, and 10% significance level, respectively
The results reported in Table 5 suggest the existence of the long-term cointegration between climate-related financial policies and circularity. This study concentrates on the nonlinear impacts of CP on circulation in the subsequent analysis.
4. Empirical results
4.1 Climate-related financial policies and circularity
Table 6 reports the impacts of climate-related financial policies and other explanatory variables on the circularity performance. In this table, we focus on the results obtained from PCSE and FGLS models.
Linear impacts of climate-related financial policies on the circulation
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Variables | Municipal waste | Circulation patents | Circular material usage | Recycling waste | Recycling biowaste | Recycling ewaste vestment |
| PCSE estimates | ||||||
| L.CP | 17.17 (2.115) | 1.20 (6.591) | 80.30* (43.352) | 29.30*** (10.146) | 45.60*** (16.372) | 108.40** (49.841) |
| L.EG | 1.35*** (0.225) | 0.20*** (0.051) | 0.09 (0.213) | 0.06 (0.039) | 0.00 (0.080) | 0.05 (0.192) |
| L.TS | −4.34 (8.845) | −5.32*** (1.040) | −64.62*** (9.604) | 2.78** (1.367) | 4.80** (2.302) | 16.06** (7.171) |
| L.FDI | 15.41 (11.487) | 1.52 (1.780) | 59.57** (23.552) | 3.31 (2.399) | 2.02 (1.984) | 17.06** (7.423) |
| L.IND | −1.97*** (0.003) | 52.58*** (15.328) | −1.04 (29.796) | 25.45*** (7.587) | −15.65 (11.731) | −119.14*** (39.691) |
| L.GE | 10.96** (4.527) | −3.10* (1.835) | −23.63*** (3.821) | −0.01 (0.964) | 14.90*** (2.744) | 46.78*** (5.039) |
| Observations | 136 | 128 | 112 | 128 | 135 | 135 |
| No. of economies | 17 | 16 | 15 | 16 | 17 | 17 |
| FGLS estimates | ||||||
| L.CP | 17.17 (6.843) | 1.20 (8.315) | 80.30* (48.664) | 29.30*** (6.672) | 45.60*** (12.070) | 108.40** (42.898) |
| L.EG | 1.35** (0.559) | 0.20*** (0.067) | 0.09 (0.372) | 0.06 (0.053) | 0.00 (0.070) | 0.05 (0.250) |
| L.TS | −4.34 (17.184) | −5.32*** (1.524) | −64.62*** (12.808) | 2.78** (1.223) | 4.80** (2.153) | 16.06** (7.651) |
| L.FDI | 15.41 (29.353) | 1.52 (2.364) | 59.57** (26.597) | 3.31* (1.897) | 2.02 (3.660) | 17.06** (13.008) |
| L.IND | −1.97*** (161.365) | 52.58*** (17.856) | −1.04 (89.634) | 25.45* (14.327) | −15.65 (20.120) | −119.14* (71.506) |
| L.GE | 10.96 (21.242) | −3.10 (1.938) | −23.63** (11.959) | −0.01 (1.555) | 14.90*** (2.692) | 46.78*** (9.567) |
| Observations | 136 | 128 | 112 | 128 | 136 | 136 |
| No. of economies | 17 | 16 | 15 | 16 | 17 | 17 |
Note(s): ***, **, and * represent 1, 5, and 10% significance level, respectively
Regarding the PCSE and FGLS estimations, Table 6 demonstrates the linear influence of climate-related financial policies and circularity performance, which is measured by municipal waste (CE_MW); recycling and secondary raw materials (CE_PA); circular material usage (CE_MA); recycling waste performance (CE_RW) and recycling share of biowaste and e-waste (CE_RB and CE_RE, respectively). In both estimations, CP has a significant and strong positive influence on CE_MA, CE_RW, CE_RB and CE_RE, with the coefficients being 80.30, 29.30, 45.60 and 108.40, respectively, at a 1% significance level.
Investigating the control variables, conclusions obtained from both the PCSE estimation and the FGLS estimation are relatively identical. Economic growth (EG) has a statistically significant positive impact on only CE_MW and CE_PA. Another factor to be taken into consideration is trade share (TS). While trade share can decrease the number of circulating patents and circulating material usage, it contributes to increasing the recycling of waste, biowaste and ewaste vestments. In addition, FDI is recorded to influence CE_MA and CE_RE positively. IND leads to a decrease in municipal waste and recycling ewaste vestment. Meanwhile, this results in more circulating patents and recycling waste. There is a difference between PCSE estimation and FGLS estimation for the level of democratization index. In both estimations, GE has a positive influence on CE_RB and CE_RE and a negative impact on CE_MA. In the PCSE estimation, GE positively impacts CE_RW but negatively impacts CE_PA. However, in the FGLS, the relationships between GE and these two indexes do not exist.
4.2 Robustness checks
4.2.1 Short-run and long-run effects: dynamic fixed-effect autoregressive distributed lag model.
In the subsequent analysis, this paper concentrates on measuring the effect of climate-related financial policies on circularity performance in the short- and long term. Results obtained from the DFE-ARDL model are summarized in Table 7.
The influence of climate-related financial policies on circularity: short-run and long-run effects
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Variables | Municipal waste | Circulation patents | Circular material usage | Recycling waste | Recycling biowaste | Recycling ewaste vestment |
| The short-term effect | ||||||
| EC term | −0.14* (0.083) | −0.55*** (0.107) | −0.50*** (0.118) | −0.52*** (0.119) | −0.22** (0.103) | −0.64*** (0.111) |
| D.CP | 1.82 (1.330) | 0.40 (0.280) | −2.16*** (0.555) | −0.26 (0.265) | −0.44 (0.439) | 0.10 (0.160) |
| The long-term effect | ||||||
| CP | 31.69** (12.394) | 0.85*** (0.230) | 4.52*** (0.242) | 0.52*** (0.068) | 1.45*** (0.254) | 0.12** (0.049) |
| Observations | 144 | 144 | 144 | 144 | 144 | 144 |
Note(s): Standard errors in parentheses.
***p < 0.01;
**p < 0.05 and
*p < 0.1. DFE-ARDL is used
In the short term, CP only has a statistically significant effect on CE_MA, which implies that increasing the number of financial policies will lead to a decrease in the share of circular material use. In the meantime, the relationships between the climate-related financial policies and CE_MW, CE_PA, CE_RW, CE_RB and CE_RE are not empirically apparent in the short run. However, in the long term, climate-related financial policies affect all six dimensions of circularity performance. In particular, the impact of CP on municipal waste, circulation patents, circular material usage, recycling waste, recycling biowaste and recycling ewaste investment are statistically significant at 5% and 1% and positive, with the coefficients being 31.69, 0.85, 4.52, 0.52, 1.45 and 0.12, respectively. The results suggest a favorable effect of climate-related financial policies, but it is more likely to exist in the long term.
4.2.2 Endogeneity control: two-step general method of moment.
Finally, this paper deals with the potential existence of endogeneity that arises from a simultaneity between climate-related financial policies and circularity. To address this issue, this study follows Hong Nham and Ha (2022) to use a two-step GMM estimate and represent the results in Table 8.
Two-step GMM estimates
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Variables | Municipal waste | Circulation patents | Circular material usage | Recycling waste | Recycling biowaste | Recycling ewaste vestment |
| CP | 6.1208* (3.306) | 1.5678* (0.946) | 0.0876** (0.001) | 0.3121** (0.008) | 1.1656*** (0.001) | 0.2207** (0.005) |
| EG | 6.3886 (8.505) | −0.9706 (0.666) | 7.9766* (4.523) | 1.7347 (9.207) | 3.0484 (2.317) | 5.7487 (7.192) |
| TS | 1.519* (0.827) | −13.596 (23.018) | −2.459 (158.179) | −1.195 (2.186) | 37.1681 (40.344) | −13.478 (16.903) |
| FDI | −4.4666 (291.994) | 345.5854** (145.034) | 52.6791 (33.056) | 53.3517 (66.930) | 14.0081 (14.834) | 78.2295 (77.018) |
| IND | −8.445* (4.635) | −3.017** (2.363) | 3.852* (2.910) | 8.071 (1.880) | 1.549 (0.122) | 1.625 (1.236) |
| GE | 2.1036 (2.968) | −4.4842* (2.3611) | −1.9892* (1.0964) | −5.5932 (5.8813) | −1.1561** (0.7025) | 1.3243 (5.1929) |
| Observations | 136 | 128 | 112 | 128 | 136 | 136 |
| No. of economies | 17 | 16 | 15 | 16 | 17 | 17 |
Note(s): ***, **, and * represent 1, 5, and 10% significance level, respectively
The impact of CP on all six dimensions of circularity is statistically significant. CP has a significant and strong positive influence on CE_MW, CE_PA, CE_MA, CE_RW, CE_RB and CE_RE, with the coefficients being 6.12, 1.56, 0.08, 0.31, 1.16 and 0.22, respectively. It can be seen that the nexus between climate policy and circularity becomes more statistically significant after controlling the potential issues of endogeneity. Regarding other control variables, while economic growth leads to an increase in circular material usage, trade share results in more municipal waste. There is a significant and positive relationship between FDI and circulation patents. IND has been reported to reduce municipal waste and circulation patents; however, it causes more circular material usage. GE results in fewer circulation patents, less circular material usage and decreased recycling biowaste.
5. Discussion and recommendations
This paper is the first to examine the relationship between climate-related financial policies and circularity performance in the European region. This study offers significant implications for policymakers in their efforts to foster and promote circularity performance in the European region. Transitioning to a CE will undoubtedly present challenges and difficulties. Among many challenges, the financial issues are critical, which hinder these countries from implementing the circularity initiatives. Hence, to mitigate any potential risks, a CE transition, particularly concerning innovation activities and financial support, must be carried out with fairness, openness and transparency. Furthermore, European countries’ governments and authorities should actively encourage and enhance their capabilities to capitalize on opportunities created by the implementation of these climate-related financial policies. According to Aranda-Usón et al. (2019), the availability of funds, the quality of the firm’s financial resources and public subsidies have a positive effect on stimulating the implementation of CE initiatives in businesses. Green and CE transitions must be costly, which is why governments must provide technical and financial support. Among all approaches and tools to support nations in achieving the goal, we strongly recommend prioritizing the improvements of climate-related financial policy performance. Enhancing available knowledge is pivotal in facilitating a country’s transition toward sustainable development and contributing to a greener world.
More notably, the government’s financial investment process must be continuous and consistent and should take place over a long enough period of time for investments to begin to demonstrate their role clearly. Otherwise, if the investment is only temporary, the financial investments are wasteful and ineffective. In other words, the governments in these countries should propose a long-term plan with detailed strategies for each period and must ensure that these strategies are implemented. There are several significant obstacles to the green transition and the transition of the region to a CE and carbon neutrality related to energy efficiency and pollution. In the Recovery Plan for Europe, 30% of the huge budget will be invested in climate change to achieve zero GHG emissions by the year 2050. For this ambitious plan to be achieved, it will be necessary to invest in research and innovation. Hence, implementing circularity and carbon neutrality should be considered as an effective long-term investment policy of the EU.
6. Conclusions
To achieve the development goals by 2030, European economies should place a high priority on sustainable growth. These countries’ aggressive economic progress necessitates substantial energy and raw material demands, posing environmental concerns such as resource exploitation and garbage generation. In spite of the country’s recent great economic success, its environmental impact has been alarming. The adoption of a CE strategy, however, requires careful consideration when selecting the policy tool and investment plans. This paper examines the relationship between climate-related financial policies and circularity performance in the European region.
To reflect the circularity performance, this article uses six different measures. These measures include the amount of municipal waste, the number of circularity patents, the amount of circular material used, the rate of recycling waste, the rate of recycling biowaste and the rate of recycling e-waste. By using various econometric techniques (namely a PCSE model, an FGLS estimates model and the two-step GMM) for the database of 17 countries from 2012 to 2020, the study indicates that climate-related financial policies are an enabler of circularity. This paper provides empirical evidence that climate-related financial policies play a critical role in enhancing a country’s circularity performance, especially in the long term.
As a result of our findings, economists and policymakers should be aware that climate-related financial policies can be an effective tool to promote a country’s circularity performance. On the policy front, climate-related financial policies should be implemented more efficiently. A further recommendation is that the government provide information to these companies regarding the availability of financing mechanisms that will allow them to improve their efficiency. Specifically, they should develop incentive mechanisms for improving the environmental performance of companies and promote environmentally friendly technologies and processes. As part of the program, companies are also advised on how to reduce their pollution through national and international financing mechanisms. Notably, the governments should propose a long-term plan with detailed strategies for each period and must ensure that these strategies are implemented.
The results of our research should be interpreted in light of two limitations. As a first limitation, we used archival data that was only available for the EU area. Developing countries where environmental degradation has been warned should consider the role of climate policies in pursuing the goal of a CE. Unfortunately, there are no surveys or related databases that adhere to strict guidelines to collect information regarding the implementation of climate policy or measurement of circularity performance in developing economies. Secondly, it may be necessary to discuss this mechanism in greater detail. It is possible that climate policies will have further effects on the implementation of circularity initiatives.
In further study, a number of factors should be considered, including the level of economic development, the performance of economic complexity and the effectiveness of government policies. A study of these channels will provide economists and policymakers with insight into how to design policies to implement climate policies and enhance circularity performance. Further research may examine the data sources to collect additional information about climate policies in developing countries and examine the role of climate policy advantages in this area.
The authors thank the National Economics University for their financial support of this paper. The authors would also like to thank the editor (with many detailed comments and suggestions) for this issue and other reviewers for providing suggestions that improved the overall coherency of the article. Without their comments and suggestions, we cannot improve the quality of paper to satisfy the journal’s requirements.
Funding: The paper was supported by the National Economics University.
Availability of data and materials: The data sets used and/or analyzed during this study are available from the corresponding author upon reasonable request.
Competing interests: The authors declare that there are no competing interests.
Author contributions: All authors contributed to all stages of preparing, drafting, writing and revising this review article. L.T.H. and N.H.Y. have made a substantial, direct and intellectual contribution to the work during different preparation stages. N.H.Y read, revised and approved the final version of this manuscript.
