1. Introduction
Environmental sustainability has evolved into a primary goal of global economic and policy frameworks, propelled by the pressing necessity to confront climate change, biodiversity decline, and resource exhaustion. The circular economy has acquired considerable prominence as a policy tool and economic model to promote sustainability in both rich and developing countries [
1]. This transition from the conventional linear model of “take, make, dispose” to a regenerative economic system seeks to dissociate economic activity from environmental degradation. The circular economy seeks to improve resource efficiency, extend the life of products, and recover value from waste, therefore reducing GHG emissions and conserving natural resources [
2]. With the intensification of global environmental issues, the transition to a circular economy has become essential.
The European Union (EU) and its member states have played a pivotal role in promoting circular economy policy. The European Green Deal, the Circular Economy Action Plan, and national resource efficiency frameworks underscore the EU’s political commitment to integrating circularity as a core element of sustainable development [
3]. Countries globally, such as China, Canada, and Japan, have embraced circular economy concepts, each emphasizing distinct priorities. For instance, China’s strategy is closely associated with industrial advancement and waste management reform, whereas Japan emphasizes resource efficiency within the sound material-cycle society framework. Despite this global momentum, the worldwide circularity rate remains alarmingly low at only 7.2% [
4]. This illustrates the persistent dominance of linear economic practices and underscores the necessity for a deeper empirical comprehension of the effects of circular economy strategies on environmental outcomes, especially GHG emissions.
Climate change has become a crucial problem of our era. The acceleration of global warming, rising sea levels, and the heightened frequency of extreme weather events have compelled countries to implement rigorous emission reduction objectives. Germany distinguishes itself by pledging to attain net-zero GHG emissions by 2045. The country has set intermediate targets to decrease emissions by a minimum of 65 percent by 2030 and 88 percent by 2040 relative to 1990 levels. Germany aspires to achieve net-negative emissions by 2050. As the largest economy in Europe and a global leader in manufacturing and innovation, Germany plays a pivotal role in the global environmental agenda [
5]. Germany’s substantial industrial sector, robust environmental regulations, and early adoption of circular economy methods provide a good foundation for analyzing the interconnections between economic developments, resource efficiency, and emission reduction [
6]. Furthermore, Germany has a strong empirical framework owing to its extensive environmental data infrastructure, bolstered by Eurostat and state agencies, for high-frequency, dependable research. The diversified industries, encompassing both energy-intensive manufacturing and innovative green technologies, provide a good context for analyzing the impact of structural and policy changes on emissions.
This study investigates the complex relationships between the circular economy, natural capital, structural economic change, environmental taxation, and industrial operations within the context of Germany’s sustainability strategy and their influence on GHG emissions over time. While several studies have examined these components independently, few have investigated them within a unified empirical framework that includes their interaction effects and dynamic temporal relationships. Furthermore, contemporary research often relies on conventional indicators such as recycling rates or trash quantities to denote the efficacy of the circular economy.
To guide this inquiry, the study draws upon two foundational theories in environmental economics: the Ecological Modernization Theory (EMT) and the Environmental Kuznets Curve (EKC) hypothesis of [
7]. EMT asserts that environmental protection and economic development are not incompatible but can be concurrently achieved via technology innovation, institutional change, and proactive environmental governance [
8]. According to EMT, advanced economies such as Germany can spearhead the transition to sustainable development by incorporating environmental considerations into fundamental economic and industrial practices. This includes the use of sustainable technology, the promotion of circular production models, and the establishment of legislative frameworks that internalize environmental externalities. Germany’s extensive waste regulations, carbon pricing strategies, and dedication to innovation-driven decarbonization show the EMT in practice.
The EKC hypothesis posits that environmental degradation typically escalates during the initial phases of economic development but subsequently decreases as income levels increase and societies allocate resources toward cleaner technologies and more stringent regulations. The EKC framework plays a crucial role in analyzing the emissions trajectory of nations experiencing economic transitions [
9]. This study expands the EKC model by integrating the circular economy and natural capital indicators as variables that could potentially expedite the transition toward environmental improvement. By highlighting how circular practices and ecosystem conservation can reduce emissions even in highly industrialized contexts, the study offers a novel interpretation of EKC dynamics.
Natural capital, encompassing biodiversity, soil, aquatic systems, and forests, is vital for climate regulation and carbon sequestration. Natural capital is fundamental to the operation of all economic systems and immediately affects climate mitigation and adaptation initiatives. The degradation of natural capital hastens biodiversity loss and ecological imbalance; hence, it undermines the resource foundation necessary for sustained economic activity [
10]. Natural capital underpins the functionality of all economic systems and directly influences climate mitigation and adaptation efforts. Degradation of natural capital accelerates biodiversity loss and ecological imbalance and undermines the resource base required for sustainable economic activity [
11]. Within Germany’s sustainability plan, the preservation of natural capital is regarded as crucial for enduring ecological resilience and intergenerational equality. The forests of Germany, covering about one-third of its territory, absorb around 62 million tons of carbon each year. It is essential to protect and enhance these ecosystems to decrease emissions and foster resilience against climate-related disturbances. This study incorporates natural capital as a crucial element, alongside circular material utilization and structural transformation, with the objective of providing comprehensive knowledge of how these interconnected domains influence GHG emission patterns.
Structural changes, including the shift from a fossil-fuel-dependent economy to one reliant on renewable energy, circular production models, and digital innovation, are concurrently altering the country’s emission profile [
12]. The energy sector, traditionally the greatest emitter, has seen a substantial decrease in emissions due to the expansion of renewables, which accounted for almost 46% of gross power consumption in 2022. The current phase-out of coal and the advancement of green technologies signify a wider economic transition [
13]. The structural changes, along with the conservation of natural capital, constitute the foundation of Germany’s climate policy. Analyzing the interaction of these dynamics is crucial for assessing the nation’s advancement in achieving climate objectives and recognizing avenues for enhanced decarbonization. To comprehensively assess the impact of these dynamics, this study employs WCA to capture the time–frequency co-movements between circular economy indicators and GHG emissions, thus identifying both short-term fluctuations and long-term linkages. In addition, FMOLS and DOLS estimators are applied to evaluate the long-run equilibrium relationships, correcting for potential endogeneity and serial correlation. While earlier studies have emphasized the theoretical promise of the circular economy, they often overlook the temporal evolution and dual-scale effects of such practices on emissions. This study addresses that gap by empirically examining how circular economy measures interact with GHG emissions across multiple time horizons, providing a more nuanced understanding of their role in achieving Germany’s climate objectives.
By doing so, our study contributes to the body of existing literature on environmental economics in the following ways: First, it offers a comprehensive empirical framework that evaluates the contributions of circular economy practices, natural capital, structural changes, environmental taxation, and industrial activity in shaping GHG emissions, an approach that remains underexplored in prior research. Second, by focusing on Germany, a highly industrialized and policy-driven economy, the study provides context-specific evidence from a major emitter, filling a regional gap in the literature. Third, this study employed the WCA, which is essential for identifying dynamic, time–frequency relationships between variables, enabling researchers to observe the evolution of correlations over time and across various time scales. This method is significant in environmental studies as it effectively captures both short-run fluctuations and long-run trends in the interactions between factors such as GHG emissions and economic indicators. Fourth, the study improves on traditional waste-based metrics by introducing the circular material use rate as a more accurate stand-in for circular economy success. Fourth, we used FMOLS and DOLS to check the robustness of our results. The results provide policymakers with useful information for decarbonization and sustainable growth.
The remainder of the paper is as follows:
Section 2 provides the literature review.
Section 3 provides the methodology.
Section 4 gives the details of results and discussions, and
Section 5 concludes this research.
3. Research Methodology
3.1. Theoretical Framework
The linear economy, defined by its “take-make-dispose” framework, continues to dominate the current economic system. This model entails the extraction of raw materials, the production of goods, their consumption, and, ultimately, the disposal of these products as waste after their functional lifespan has ended. This system results in the unnecessary depletion of natural resources, as each cycle of production and disposal contributes to the gradual loss of these vital resources [
40]. The conventional linear model generally entails the acquisition of material resources, their conversion into consumable products, and their eventual disposal, often neglecting the long-term ecological consequences [
41]. This model emphasizes financial profit and consumer convenience at the expense of environmental sustainability, leading to the creation of products typically intended for single use and subsequent disposal.
The circular economy represents a significant shift addressing the inefficiencies and environmental harm associated with the linear economic model. This approach promotes a restorative system aimed at reducing waste and enhancing resource efficiency through a reevaluation of product lifecycles. The literature indicates that transitioning from a linear to a circular economic model necessitates addressing several significant challenges, including technological limitations, economic issues, and changes in behavior [
41]. Comprehending these challenges is essential for the effective implementation of circular practices and their subsequent success.
The circular economy is based on the principle of utilizing resources to foster regeneration and renewal instead of depletion. It highlights innovative methods such as recycling, reusing, and remanufacturing to transform waste into valuable inputs. The circular economy presents a viable framework for addressing issues of resource scarcity, waste generation, and inefficiencies inherent in conventional economic systems [
42]. Highlighting recycling and other circular economy practices is essential for ensuring environmental sustainability and reducing harmful emissions. The implementation of these practices can markedly decrease the ecological footprint associated with industrial and consumer activities [
25].
Considering the growing significance of environmental management within the industrial sector, it is crucial to evaluate the roles and contributions of different stakeholders in effective waste management. The literature emphasizes the necessity of a collaborative approach among governments, corporations, and consumers for the effective implementation of circular economy models [
43]. Governments are responsible for establishing regulatory frameworks and incentives that promote circular practices, whereas businesses must innovate to integrate circular principles into their operations. Consumers play a vital role by adopting sustainable behaviors and endorsing products intended for reuse and recycling [
44].
The circular economy aims to enhance the efficiency and sustainability of limited natural resources, such as forests, land, water, air, metals, and minerals. This model reduces emissions linked to resource extraction and processing by minimizing resource consumption and waste generation. This approach addresses urgent environmental challenges while also considering social dimensions and promoting human well-being [
45]. The transition from a linear economy characterized by a “manufacturing-consumption-waste” model to a circular economy defined by “make-use-recycle” necessitates the integration of sustainable principles that emphasize resource productivity and lifecycle management. The circular economy model is defined by strategies aimed at minimizing or eliminating resource flows, thereby reducing the negative environmental impacts linked to conventional economic practices [
27].
In summary, transitioning from a linear to a circular economy represents a significant shift in resource management and utilization. The circular economy emphasizes sustainability and efficient resource utilization, providing a viable alternative to conventional practices. It has the potential to significantly reduce environmental harm and promote long-term ecological sustainability.
3.2. Model Specifications
This study is grounded in the theoretical frameworks of ecological modernization and circular economy theory. Ecological modernization asserts that technological innovation, institutional transformation, and market-driven strategies can decouple economic growth from environmental degradation. The circular economy is a systems-oriented approach aimed at closing material loops, enhancing resource efficiency, and minimizing waste and pollution. The circular material use rate, environmental taxation policies, and structural economic variables utilized in this study serve as practical applications of these theoretical concepts. The study integrates principles of sustainable development, highlighting the balance of environmental integrity, economic efficiency, and social responsibility, consistent with the triple-bottom-line framework. This study seeks to evaluate the effects of circular economy practices on emissions outcomes through the integration of various perspectives.
Building upon the insights derived from the reviewed literature, this study adopts an enhanced analytical framework to examine the relationship between circular economy practices and GHG emissions. Diverging from prior research that predominantly centered on CO
2 emissions as the sole indicator of environmental impact, this study employs GHG emissions as the dependent variable to capture a broader and more comprehensive spectrum of emissions contributing to climate change. Furthermore, while earlier studies have often utilized waste management indicators to represent circular economy activities, this research introduces the circular material use rate as the principal independent variable. This methodological refinement enables a more direct and nuanced assessment of how material circularity affects aggregate GHG emissions. The empirical specification of the model is presented in Equation (1):
where
,
,
,
,
, and
denote GHG emissions, circular economy, natural capital, structural changes, environmental tax, and industrial activities, respectively.
denotes the constant term, whereas
, …,
represent the parameters that need to be estimated, and
is the error term.
3.3. Data
The dataset employed in this study comprises quarterly observations for Germany spanning the period from first quarter of 2010 to fourth quarter of 2022. Annual series were converted to quarterly frequency using the quadratic match-sum interpolation method, which enhances temporal granularity while preserving the integrity of annual aggregates. To improve the statistical properties of the data and facilitate meaningful interpretation, all variables were transformed into their natural logarithmic forms. This transformation helps stabilize variance, supports model linearity, and enables the interpretation of estimated coefficients as elasticities, thereby capturing proportional relationships among the variables within the econometric framework.
The dependent variable in this analysis is GHG emissions, measured in tons per capita and obtained from the Eurostat database. Unlike studies that focus solely on CO
2 emissions, this broader metric captures the full spectrum of anthropogenic emissions contributing to climate change, thereby offering a more comprehensive assessment of environmental impact. To evaluate circular economy performance, this study adopts the circular material use rate as the key explanatory variable. Consistent with recent empirical work, such as Abbas and Imran [
2] and Neves and Marques [
46]. It is defined as the share of materials recovered and reintegrated into the production cycle. Data for this variable are also sourced from Eurostat, reflecting material circularity and resource efficiency within the economy. This rate quantifies the ratio of recycled materials reintegrated into the economy compared to the total materials utilized, underscoring the efficacy of circularity initiatives in diminishing dependence on new raw resources. Although we recognize that this proxy reflects but one facet of circularity, specifically material recirculation, it was chosen for its reliability, data accessibility, and formal application in European monitoring frameworks. To address the inherent limitation of depending on a singular indicator, we augment it with additional variables such as environmental tax revenue, industrial structure, and natural capital, which represent further dimensions of the circular economy, including institutional support, sectoral dynamics, and environmental sustainability. This multivariable technique guarantees a more thorough analysis while upholding scientific integrity and data reliability.
Natural capital, as defined by the United Nations Conference on Trade and Development (UNCTAD), refers to the economic value derived from a country’s stock of natural resources, including oil, natural gas, coal, minerals, forests, and agricultural land. It denotes the economic value generated from a nation’s inventory of natural resources, encompassing oil, natural gas, coal, minerals, forests, and agricultural land. Total natural resource rents are often quantified as a percentage of GDP, reflecting the income derived from the extraction and utilization of these resources, after accounting for extraction costs. This measure indicates the extent of a nation’s dependence on extractive and land-based activities for economic production. An increased proportion of natural resource rents in GDP signifies a greater reliance on natural capital, perhaps indicating susceptibility to commodity price volatility and sustainability issues if resource management is ineffective. The data on natural capital, accessible via the UNCTAD database, function as an effective proxy for evaluating the economic importance of natural resources and the possible environmental and structural consequences of resource reliance [
47].
Structural changes denote the protracted transition of economic activity from low-productivity sectors, such as agriculture, to higher-productivity sectors, including manufacturing and services. This shift is essential for attaining sustained economic growth and development. UNCTAD evaluates structural change using variables like the sectoral composition of GDP and employment, the proportion of industry and services in economic output, the degree of export diversification and complexity, and the intensity of fixed capital creation. These indicators demonstrate the redistribution of resources among sectors and enhancements within sectors. Structural transformation entails recognizing and mitigating binding constraints, like insufficient infrastructure, restricted technical capability, and regulatory obstacles that hinder productivity improvements. By monitoring these developments, UNCTAD offers a framework for comprehending the evolution of economies toward more intricate and higher value-added industries. The data on natural capital and structural changes are freely available on the United Nations Conference on Trade and Development website (
https://unctad.org/topic/least-developed-countries/productive-capacities-index) (accessed on 2 March 2025).
The role of environmental tax revenue is incorporated into the analysis to assess its impact on GHG emissions. This variable, measured as a percentage of total tax and social contribution revenues, serves as an economic instrument designed to internalize environmental externalities. By imposing a levy on each ton of GHG and CO2 emitted, environmental taxation aims to incentivize polluters to adopt more efficient and cost-effective abatement strategies. In doing so, it underscores the potential of fiscal policy to drive environmental improvements and promote sustainable practices within the economy. To measure the impact of industrialization, we use industrial activities as a proxy for industrialization (the data were collected from Eurostat).
3.4. Methodological Framework
The methodological framework employs advanced econometric techniques to analyze the relationship between GHG emissions and key explanatory variables. By incorporating both primary and control variables, the approach ensures a robust and comprehensive evaluation of their effects on environmental outcomes.
3.4.1. Unit Root Testing
Unit root tests play a crucial role in time series analysis by assessing the stability of data, a fundamental assumption that significantly influences the precision of statistical conclusions and forecasts. The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests are frequently utilized to identify unit roots, providing insights into the temporal characteristics of the series. Detecting non-stationarity via these tests aids in avoiding misleading regression outcomes and informs the choice of appropriate modeling techniques.
The ADF test is defined by the following Equation (2):
where
denotes the vector of deterministic components. This test assesses whether a unit root exists in the time series, allowing for the evaluation of data stationarity. Similarly, the PP test is expressed as indicated in Equation (3):
where
denotes the first differences of
,
is the coefficient of the lagged series
, and
is the error term. Both tests play a crucial role in assessing the long-term characteristics and trends within the data, confirming that the assumptions supporting the statistical analysis are both valid and reliable.
3.4.2. Cointegration Testing
This study employed the Johansen cointegration test, which was developed by Johansen and Juselius [
48] and Johansen [
49]. This test is considered a powerful statistical method used to examine the presence of long-run equilibrium relationships among multiple non-stationary time series variables that are integrated in the same order, typically I(1). Unlike other cointegration techniques that can only analyze two variables, the Johansen test enables the simultaneous evaluation of multiple variables within a vector autoregressive framework, which makes it particularly useful in multivariate settings. One of its key advantages lies in its ability to determine not just the existence of cointegration but also the number of distinct cointegrating vectors, which offers a deeper understanding of the dynamics among variables. The test offers two approaches, such as the trace test and the maximum eigenvalue test, each providing evidence for the number of cointegrating relationships. This method is crucial in time series analysis because it helps researchers avoid spurious regressions by ensuring that only variables with a valid long-term association are modeled together. Overall, the Johansen test is important for analyzing related time series data, like in economics or finance, where it is vital to understand both long-term balance and short-term changes.
3.4.3. Wavelet Coherence Analysis
WCA has emerged as a significant approach in economics for analyzing the dynamic relationships and interactions among economic time series. Initially formulated by Goupillaud and Grossmann [
50], the WCA has been extensively utilized in various fields, including economics, owing to its capacity to capture time-varying associations. Economic variables frequently undergo changes and exhibit varying relationships over time. WCA facilitates the examination of how these relationships and their timing differ across various time periods and frequencies. In contrast to cross-wavelet analysis, which is limited to specific frequency points due to scale smoothing, WCA effectively captures correlation patterns across a wider frequency spectrum, providing valuable insights into cycles and relationships within economic data. When applied alongside econometric methods such as FMOLS and DOLS, which emphasize long-term relationships, WCA provides supplementary insights by revealing complex, time-varying connections that traditional methods may fail to capture. This combination enables a more detailed and thorough understanding of complex economic phenomena.
The Morlet wavelet family, which has the following mathematical definition, is used to introduce WCA in the present study. The Morlet wavelet, which is used in this research and described in Equation (4), makes it possible to examine economic variables at various time and frequency levels and get a thorough grasp of their interactions:
where
denotes the wavelet function,
serves as normalization constant,
corresponds to a complex sinusoid with Gaussian unit standard deviation,
represents the Gaussian envelope, and
defines the central frequency of the wavelet. This formulation integrates a sinusoidal oscillation modulated by a Gaussian window, enabling precise localization in both time and frequency domains. Subsequently, the original time series is transformed into the time–frequency space through convolution with scaled and translated versions of the wavelet function.
is thereby mapped into
as formalized in Equation (5):
where
represents the wavelet function localized in both time and frequency domains, where
indicates its position in the time domain
denotes the corresponding frequency component, and
t is the time variable. The normalization factor is given by
, which ensures energy preservation across different scales. To further explore the dynamic interactions between two time series, the cross-wavelet transform (CWT), denoted as (
is introduced and formalized in Equation (6). This transformation enables the identification of time-varying coherence and phase relationships, providing a detailed understanding of their co-movement and synchronization across multiple time–frequency scales:
where
represents the original time series, which is analyzed using the formulation presented in Equation (7):
Furthermore, the wavelet power spectrum (WPS), as defined in Equation (8), measures the distribution of variance within a time series across both time and frequency domains:
where
denotes the wavelet power spectrum. The next step entails estimating the dependency structure in the time–frequency domain through the application of CWT power analysis as given by Equation (9):
where
represents the CWT of two time series in the time–frequency domain.
and
denote CWT of the individual time series at
and
, respectively. The magnitude and consistency of their co-movement is calculated through WCA, expressed by
in Equation (10):
The strength of the interaction between two time series is measured using WCA, defined by
in Equation (11). This metric represents the squared correlation coefficient in time–frequency domain ranging from 0 to 1 (0 ≤
≤ 1). A value of
close to 1 indicates a strong correlation at a specific time and frequency, typically illustrated in red and enclosed black contour lines. Conversely, value near 0, shown in blue, suggest weak or no correlation. The smoothing function C ensures stability of coherence estimate over time. Additionally, the phase angle, defined in Equation (11), provides the lead–lag relationship between the two variables:
where
denotes the phase angle. The equation represents the ratio of the imaginary component
and real component
of CWT.
3.4.4. Robust Analyses
FMOLS and DOLS are advanced statistical methods designed to provide accurate and fair estimates of long-term connections in systems that are cointegrated. FMOLS reduces issues of endogeneity and serial correlation by making semi-parametric changes to the standard OLS estimator, resulting in estimates of the cointegrating vector that are nearly unbiased and reliable as the sample size increases. FMOLS reduces problems of endogeneity and serial correlation by making adjustments to the standard OLS estimator, which helps provide reliable and accurate estimates of the cointegrating vector over time. Both methods are primarily focused on accurately estimating long-run equilibrium relationships while correcting for statistical issues that commonly affect non-stationary time series. Equation (12) presents the FMOLS and DOLS:
where
represent the estimators derived from the FMOLS and DOLS methods, respectively. The corresponding t-statistics for these estimators can be computed as outlined in Equation (13):
5. Conclusions and Policy Implications
5.1. Concluding Remarks
Germany is instrumental in the EU’s endeavors to mitigate GHG emissions by advocating for a circular economy. Germany, the EU’s largest economy and a leader in industrial innovation, establishes standards for sustainable practices by incorporating resource efficiency, waste reduction, and recycling into its production and consumption systems. Its robust policies regarding waste management, eco-design, and extended producer responsibility not only contribute to the reduction of emissions within the country but also have a significant impact on the development of EU-wide regulations and best practices. Germany makes a substantial contribution to the EU’s climate objectives by advocating for circular economy principles, which are instrumental in the continent’s transition to a resource-efficient, low-carbon future.
This study employs WCA, FMOLS, and DOLS to examine the long-term and dynamic relationships between the circular economy, natural capital, structural changes, environmental tax, industrial activities, and GHG emissions in Germany from first quarter of 2010 to fourth quarter of 2022. The Johansen cointegration test using FMOLS and DOLS confirms the existence of a stable long-run equilibrium relationship between these variables. WCA also reveals the frequency-specific and time-varying co-movements between the explanatory variables and GHG emissions, illuminating how these connections change across various time periods. The findings show that industrial operations significantly and favorably affect GHG emissions, but environmental taxes, the circular economy, structural modifications, and the preservation of natural capital all have a negative effect on emissions. By optimizing material flows and lowering dependency on non-metallic minerals and fossil fuels, these results demonstrate the critical role that circular economy practices play in lowering GHG emissions.
5.2. Policy Implications
This section outlines five policy implications for Germany aimed at reducing GHG emissions through the integration of circular economy principles, natural capital considerations, and structural changes, including:
Germany ought to introduce tax incentives, subsidies, or innovation grants for enterprises that embrace circular economy practices, including product-as-a-service, remanufacturing, or zero-waste production, to separate economic growth from resource consumption and emissions. Policies should prioritize the restoration and preservation of ecosystems, such as forests, wetlands, and soils, which function as carbon sinks. Incentive programs aimed at promoting biodiversity-friendly agriculture and sustainable forestry can improve natural carbon sequestration. Structural changes must involve the transition of energy- and carbon-intensive sectors, such as steel, chemicals, and automotive, toward cleaner technologies by promoting green research and development, electrification, and circular supply chains. Germany could enhance its carbon pricing mechanisms and implement material use efficiency standards in critical sectors, ensuring appropriate valuation of natural resources and the internalization of emissions in economic decision-making. Structural economic shifts require labor market policies that facilitate retraining, education, and workforce mobility in circular and green sectors, thereby ensuring a just transition for workers impacted by decarbonization.
While macro-level recommendations such as green taxation, investment in circular infrastructure, and ecological conservation remain essential, it is equally important to assess the effectiveness of Germany’s existing regulatory instruments. The revised Circular Economy and Waste Management Act (KrWG) has laid a strong legal foundation for waste prevention, resource efficiency, and extended producer responsibility. However, implementation gaps persist in areas such as plastic recycling quality, e-waste traceability, and industrial resource loops. Policymakers should strengthen monitoring and enforcement mechanisms, expand digital product passports, and incentivize the circular design of products through stricter eco-design standards.
Furthermore, the Russia–Ukraine conflict has expedited Germany’s transition from reliance on fossil fuels, presenting a rare chance to harmonize industrial decarbonization with the objectives of the circular economy. Targeted policies could involve financial help for energy-heavy industries to produce more efficiently, rules for government buying that prioritize circular materials, and training for small and medium-sized enterprises to use circular business models. These actionable, sector-specific measures would ensure that Germany sustains its circular leadership and builds industrial resilience in a rapidly changing geopolitical and energy landscape.
5.3. Research Limitations
While this study offers meaningful insights into the relationship between the circular economy and GHG emissions in Germany, it is subject to several limitations that should be acknowledged.
First, although advanced techniques such as WCA were employed to capture temporal variations, concerns about potential endogeneity remain. The finding that the circular economy and GHG emissions influence each other indicates that there may be feedback effects, which complicates the basic assumptions used in traditional methods like FMOLS and DOLS. As these estimators are used for robustness testing, the coefficient estimates should be interpreted cautiously, as they may not fully represent the pure causal effects. Future research could improve this issue by using instrumental variable (IV) methods or system-based estimators like GMM to better manage endogeneity and enhance the understanding of cause and effect.
Second, the study relies primarily on the circular material use rate as a proxy for the circular economy. While this indicator is widely recognized and officially reported by Eurostat, it captures only one dimension of circularity, ‘material recirculation’, potentially omitting other important aspects, such as product lifespan, eco-design, and digital innovation. Even though extra factors like environmental tax revenue and industrial structure were added to improve the model, future studies could be more helpful if they created and used combined or multi-faceted measures of the circular economy.
Third, the analysis is confined to Germany and, therefore, may not be generalizable to countries with different economic structures, policy regimes, or levels of industrial development. Comparative studies across different industries, multiple countries, or regions would offer greater clarity about how circular economy practices interact with emission trends under varying institutional and environmental conditions.
Lastly, the study is based on annual data, which, while consistent and reliable, may not fully capture short-term fluctuations or intra-annual policy effects. The inclusion of higher-frequency or disaggregated sectoral data in future research could allow for more granular analysis.
5.4. Suggestions for Future Studies
Future research might build on this analysis by comparing how varied legislative settings and industry structures impact the relationship between circular economy practices and emissions in different countries. It would also be helpful to do studies on certain sectors to find out how circular strategies affect various industries in different ways. Also, using composite indicators that show more than one aspect of circularity and considering institutional and behavioral elements might provide us with a better picture. Using nonlinear models and quasi-experimental approaches would make causal inference even stronger. Simulation-based forecasting might look at the long-term consequences of different circular economy policy scenarios.