EU climate countdown: economic risks, adaptation and smart policies Open Access

The main purpose of this paper is to investigate whether climate change impacts significantly affect the macroeconomic environment and assess the manner in which environmental policies are effective in mitigating climate change risks. Nonetheless, in recent years, the academic environment has shown a growing interest in environmental studies, particularly in response to the increasing emphasis exercised by the European Commission on addressing climate change. This heightened interest is driven by the urgent need to understand and mitigate the impacts of climate change, which poses significant risks to various facets of society. Among these risks, the potential effects on the macroeconomic environment are of paramount importance.

Deriving from the existing literature, we examine climate change risks and their macroeconomic implications while also critically examining the efficiency of policies adopted at the European Union (EU) level. There are several marginal contributions to this paper. The existing literature predominantly explores various mitigation measures to diminish the impact of environmental shifts, aimed at enhancing quality of life. In contrast, our study uniquely contributes to this field of study by examining the impact of climate change risks on the macroeconomic environment whilst rigorously assessing the effectiveness of existing EU environmental policies to determine if further improvements are required. As such, we used a multiple linear regression analysis to assess the relationship between various climate change indicators and economic growth. Through this quantitative approach, we seek to clarify the extent to which changes in climate-related variables can predict variations in economic performance.

What sets our approach apart is the integration of climate science with economic modelling, providing insights into how climate-induced risks could shape future economic trajectories. This interdisciplinary approach underscores the necessity of incorporating environmental considerations into economic planning and policy-making. In addition to investigating the macroeconomic impacts of climate change, it is equally crucial to assess the efficacy of environmental policies already implemented. Within the framework of the European Green Deal, numerous policies have been enacted to mitigate climate change and promote sustainable development.

To this end, we evaluated the efficiency of these environmental policies by using a multiple indicators multiple causes (MIMIC) model to analyse how various forms of environmental expenditure have influenced climate change indicators. The MIMIC model allows for examining latent variables, providing a comprehensive understanding of the multifaceted impacts of environmental spending. By identifying both positive and negative effects, this analysis sheds light on which areas of expenditure are most effective in addressing climate change.

Our results reveal a clear and significant connection between climate change risks and economic growth. The model demonstrated the strong predictive power of the independent variables on the variability in gross domestic product (GDP) per capita growth. Notably, net greenhouse gas emissions and final energy consumption prove a positive relationship with GDP growth, while production in industry and cooling and heating degree days carry negative impacts. These findings highlight the complex interplay between environmental factors and economic performance, emphasising both the economic challenges posed by climate change and the effectiveness of environmental policies in mitigating these risks. In addition, our MIMIC model analysis confirmed that government expenditures on environmental protection significantly influence climate-related indicators, offering valuable insights into the effectiveness of EU climate policies.

Such analysis is critical for several reasons. First, it provides empirical evidence on the success of current environmental policies, highlighting areas where further improvements are needed. Second, it offers a starting point for assessing future policies, ensuring that resources are allocated efficiently to maximise positive outcomes. Finally, it reinforces the importance of continuous monitoring and assessment in achieving long-term environmental and economic sustainability. Overall, this research offers a novel, interdisciplinary approach, combining econometric methods and environmental science, to assess both the macroeconomic impacts of climate change and the efficiency of EU environmental policies under the European Green Deal framework.

Our paper is structured as follows: Section 2 provides a comprehensive theoretical framework on the main concepts and mitigation strategies adopted with respect to climate change risks. It delves into the mechanisms by which climate change impacts various economic sectors and explores the spectrum of mitigation strategies that have been proposed and implemented at an EU level. In addition, this section synthesises the predominant perspectives within the academic environment regarding the profound influence of climate change on economic growth. By integrating these viewpoints, it offers a nuanced understanding of the intricate relationship between environmental changes and macroeconomic dynamics, setting the stage for subsequent empirical analysis and policy evaluation. Furthermore, Sections 3 and 4 highlight the methodology used to achieve our research objectives. Section 3 delineates the rationale for selecting specific environmental and macroeconomic indicators, explaining their relevance and applicability in both multiple linear regression and the MIMIC approach, whilst Section 4 presents the results of our study, detailing the statistical analyses and empirical findings derived from these methodologies. Finally, Section 5 encompasses our main conclusions and highlights potential avenues for future research, offering insights into areas that could extend and deepen the understanding of the economic impacts of climate change and the effectiveness of environmental policies.

The period from 2010 to 2022 has been characterised by several significant economic impacts, shaped by a range of global events and policy developments. Notably, the decade began with a robust recovery from the 2008 financial crisis, followed by rapid technological advancements and digital transformation that reshaped industries and labour markets. Climate change emerged as a critical issue, prompting increased focus on environmental policies and sustainability measures, including international agreements like the Paris Agreement. The COVID-19 pandemic, commencing in early 2020, profoundly disrupted economic activity, leading to widespread job losses and substantial fiscal interventions. Figure 1 provides a chronological assessment of these economic impacts, offering a comprehensive overview of how these events have interrelated and influenced the global economic landscape.

Climate change risks include potential adverse consequences and vulnerabilities stemming from the perturbations in climate patterns and conditions. These risks profoundly impact diverse dimensions of our global milieu, including the natural environment, human societies, economic systems and ecological frameworks. They are characterised by their intricacy, interconnectivity and multivariate manifestations (Schneider, 2006).

One prominent aspect of climate change risks is extreme meteorological events’ escalating frequency and severity. These occurrences, ranging from hurricanes and heat waves to floods and droughts, entail the risk of physical devastation, loss of life and substantial economic perturbations. Alongside, the escalating sea levels constitute a distinct dimension of risk, culminating in coastal submersion, erosive processes and the associated threats to coastal communities and associated infrastructure.

Climate change engenders apparent shifts in temperature patterns, exemplified by higher global temperatures. Such alterations in climate parameters entail far-reaching consequences, permeating realms encompassing agriculture, human health and ecological dynamics. Biodiversity loss, another salient facet of climate change risks, signifies perturbations within ecosystems and habitats, ultimately leading to the erosion of species diversity and the potential for ecosystem collapse (United Nations, 2021).

Given the multifaceted nature of climate change and using several distinct indicators for quantification purposes, it is crucial to analyse such metrics and trends to have a comprehensive understanding of climate change and the related risks posed. As such, Table 1 summarises the most common indicators regarded as drivers of climate change quantification.

In this respect, climate change requires adaptation policies to mitigate its impacts, collectively influencing the economy’s and financial system’s structure and dynamics. Subsequently, it introduces risks to both price and financial stability, as noted by the European Central Bank in its 2021 paper and in Boneva et al. (2021) and Boneva et al. (2022). Furthermore, while focusing on price stability, monetary policy can support other EU policies, such as environmental initiatives. Climate change and the associated transition policies can influence the economy and financial system through multiple channels, making it imperative to delineate these pathways for a comprehensive understanding of the implications of climate change and the energy transition on broader economic and financial dynamics.

The European Commission, as the executive branch of the EU, has expressed a comprehensive stance on climate change risks. Within the purview of its responsibilities, the European Commission has consistently recognised climate change as a preeminent and formidable global challenge. This recognition has been manifested through a series of strategic documents, initiatives and policy measures to address the multifaceted dimensions of climate change risks.

Foremost among these initiatives is the European Green Deal (2019), a pivotal policy framework launched in December 2019. This ambitious undertaking delineates a roadmap for the EU’s transformation into a climate-neutral entity by 2050. Inherent to this endeavour is acknowledging that climate change risks constitute a paramount concern, necessitating resolute actions, including substantial reductions in greenhouse gas emissions, heightened energy efficiency and a decisive shift towards renewable energy sources. The legal framework underpinning the EU’s commitment to climate action is embodied in the European Climate Law, ratified in April 2021. This legislation legally obligates the EU to achieve climate neutrality by 2050 and articulates a structured framework for climate mitigation and adaptation actions. Importantly, it incorporates science-based targets to guide the EU in effectively addressing climate change risks.

As deemed by the literature, climate change poses a substantial threat to the current macroeconomic environment. Batten et al. (2020) argue that the risks of climate change manifest as “economic shocks”, broadly defined as an unforeseen but significant event that generates specific changes within the economy. Such economic shocks are further detailed as being both supply or demand-driven, affecting consumer spending and business investments altogether, shaping households’ and firms’ expectations about future economic outcomes (Lane, 2019) as well as production through increases in production costs. Directly, extreme weather events such as hurricanes, floods and wildfires cause immediate destruction of infrastructure, disruption of supply chains and loss of productivity. These events necessitate substantial financial outlays for recovery and rebuilding efforts, diverting resources from other economic activities. Indirectly, climate change exacerbates resource scarcity, particularly water and agricultural outputs, increasing prices and heightened food insecurity. Moreover, the long-term shifts in climate patterns force industries to adapt, often requiring costly investments in new technologies and processes. These economic impacts are unevenly distributed, disproportionately affecting developing nations and marginalised communities, exacerbating global inequality. Furthermore, the uncertainty associated with climate change and its impacts undermines investor confidence and economic stability, leading to potential fluctuations in markets and long-term growth prospects. Therefore, addressing the economic shocks of climate change requires comprehensive policy frameworks that integrate environmental sustainability with economic resilience and equity.

Qin et al. (2024) highlight that the adoption of environmental policies for green technologies purposes, such as renewable energy innovations, intelligent transportation and waste management, is presented as crucial for reducing climate risk and fostering sustainable economic growth. These advancements in technological progress are essential for decarbonising various sectors, including heavy industry and transportation, by promoting low-carbon solutions through electrification and carbon capture. This aligns with the growing recognition of the potential linkage between climate change and economic growth, as technological solutions become key to mitigating climate risks and enhancing long-term economic sustainability.

In the context of climate change, the post-growth perspective challenges traditional economic models that prioritise continuous growth, advocating instead for a focus on sustainability and well-being within ecological limits. This perspective argues that the relentless pursuit of economic growth is incompatible with environmental sustainability, particularly in light of climate change. According to Jackson (2009), the pursuit of perpetual economic growth in a finite world leads to environmental degradation and exacerbates climate risks. This view is supported by research indicating that conventional growth models fail to account for the ecological constraints of the planet and the limits of natural resources (Daly, 1996). In contrast, a post-growth approach emphasizes the importance of transitioning to economies that prioritise qualitative improvements in human well-being and ecological stability over quantitative economic expansion (Jackson, 2019). Such approaches often involve rethinking consumption patterns, promoting resource efficiency and fostering social equity, all of which are crucial for effectively addressing climate change and achieving long-term environmental sustainability (Schneider, 2005).

Furthermore, the literature displays a degree of uncertainty concerning the effectiveness of already-adopted environmental policies. While a consensus exists on the negative economic impacts of unregulated climate change factors (Abass et al., 2022), the precise economic benefits and costs associated with specific mitigation strategies remain debatable. This uncertainty comes from various aspects ranging from the complexity of climate systems to the diverse socioeconomic contexts in which these policies are implemented. Such uncertainty is compounded by challenges in accurately forecasting future emissions trajectories, assessing the efficacy of technological innovations and accounting for the intricate interactions between climate, economy and society. Moreover, the long-term nature of climate change necessitates evaluating policy effectiveness over extended time horizons, introducing additional layers of uncertainty regarding future conditions and outcomes. Despite these challenges, empirical evidence suggests that proactive policy measures and international cooperation and innovation can significantly enhance the likelihood of achieving climate goals. However, addressing the uncertainty surrounding policy effectiveness requires ongoing interdisciplinary research, robust modelling approaches and adaptive governance structures to navigate complex and evolving socio-environmental dynamics.

This paper aims to evaluate the repercussions of climate change risks on the macroeconomic environment, focusing on the multifaceted and interconnected effects of such risks on economic stability and growth. The assessment encompasses an analysis of both direct and indirect indicators through which climate change influences macroeconomic variables, including GDP per capita and expenditure on means to minimise the risks imposed by climate change. The study endeavours to quantify how climate change influences economic growth and identify the critical impact channels by using a thorough methodological framework that integrates econometric modelling.

To conduct empirical research on the impact of climate change on the macroeconomic environment, we have selected data for the EU from 2010 to 2021 to capture the immediate effects of recent environmental policy adoptions. The choice of countries, specifically the 27 EU member states, is rooted in their shared regulatory framework under the European Green Deal. The EU’s ambitious climate targets make it an ideal setting to study the interplay between environmental policies and economic outcomes, particularly because these countries follow harmonised environmental standards and reporting, making data collection and cross-country comparisons feasible.

The data encompasses a range of macroeconomic and environmental indicators sourced from reputable databases such as Eurostat, the International Monetary Fund and the Global Green Growth Institute. The chosen timeframe of 2010–2021 provides a robust data set to observe short- to medium-term trends and shifts resulting from policy interventions. This period includes significant EU policy milestones covered by the European Green Deal policy framework.

To define climate change throughout our research, we chose greenhouse gas emissions (CO₂ emissions) and heating and cooling degree days as the leading indicators of their crucial role in depicting global warming, based on their relevance to both the environmental and economic impacts of climate change. Greenhouse gas emissions (CO₂ emissions) and heating and cooling degree days were selected as primary climate-related indicators due to their direct representation of global warming and the strain it places on both energy consumption and infrastructure resilience. Monitoring CO₂ emissions provides insights into the progress of mitigation strategies and the effectiveness of environmental policies aimed at reducing the carbon footprint whilst also helping in understanding the long-term trends in atmospheric concentrations of these gases and their implications for global temperature rise. Further, heating and cooling degree days indicate the vulnerability of countries during global warming. Increasing heating and cooling degree days indicate shifts in seasonal temperatures, which directly affect energy consumption, infrastructure resilience and economic activities related to building maintenance and energy supply. Environmental economics theoretical foundations (Althor et al., 2016; Mourshed, 2012; Andrade et al., 2021) support the use of these variables, as they highlight economies’ increasing vulnerability to climate shifts. These indicators allow for a comprehensive evaluation of how changing climate patterns directly affect productivity, energy demands and overall economic performance.

Further, given our focus on the direct impact of climate change on economic growth, it is imperative to consider indicators that reflect both environmental and economic dimensions to analyse this impact (Table 2). To assess the direct impact of climate change on the economic sector, we also considered the volume of freight transport, industry production and final energy consumption as key indicators. These metrics illustrate the extent of pollution originating from transportation and industrial activities within EU countries.

The selection of these indicators is grounded in the theoretical understanding that sectors such as transportation and industry are among the largest contributors to greenhouse gas emissions, particularly in the EU. Freight transport is closely tied to economic activity and supply chain operations, but its environmental impact, primarily through CO₂ emissions, is substantial due to the heavy reliance on fossil fuels (Yan et al., 2021). Industrial production, which drives economic growth and technological advancement, also has environmental costs due to its energy-intensive nature and emissions of pollutants, including CO₂ (Gillingham and Stock, 2018). These indicators are critical in understanding the balance between economic activities and their environmental consequences.

The volume of freight transport is a significant source of CO₂ emission due to the reliance on fossil-fuel-powered vehicles, whilst the production in industry represents the output of manufacturing and related sectors. Industrial production is closely linked to economic growth, employment and technological advancement. However, similar to the volume of freight transports, it is also a significant contributor to environmental pollution, including emissions of CO₂, particulate matter and other pollutants. The theoretical justification for focusing on these variables stems from the economic-environmental nexus, where the industrial and transport sectors contribute to GDP, as key drivers of environmental degradation (Jorgenson and Clark, 2012). According to the Environmental Kuznets Curve hypothesis, economic growth initially leads to environmental deterioration until a certain level of income is reached, after which further growth may lead to improvements in environmental quality through increased environmental awareness and policies (Dinda, 2004).

Final energy consumption, encompassing the total energy used by end consumers, offers a comprehensive way to assess energy demand across residential, commercial, transportation and industry sectors. It helps us understand an economy’s aggregate energy requirements and their implications for climate change. Higher energy consumption typically correlates with higher emissions, making it a crucial variable in our analysis of how economic activities influence environmental outcomes. Energy consumption is a fundamental indicator in environmental economics, as it is directly linked to the carbon footprint of an economy (Kemp and Pontoglio, 2011). The inclusion of this indicator aligns with theories such as the Jevons Paradox, where increased energy efficiency can paradoxically lead to higher overall energy consumption, thus underlining the complex dynamics between economic growth, energy use and environmental sustainability (Sorrell, 2007).

Given that our research also wishes to provide insight into the effectiveness of environmental policies adopted at the EU level, we further evaluated the commitment and interest of policymakers in implementing such environmental policies by considering several expenditure indicators expressed as a percentage of GDP. Such indicators include expenditures on environmental protection, pollution abatement, waste management and wastewater management, which were deemed pivotal for understanding the allocation of resources towards sustainability initiatives and their prioritisation within national budgets. By incorporating these expenditure indicators (Table 3), our analysis comprehensively evaluates EU countries’ financial priorities and policy commitments concerning environmental sustainability. Expenditure on environmental protection is seen as a proxy for the state’s commitment to mitigating climate risks, reflecting the extent to which financial resources are allocated towards achieving sustainability goals.

Such indicators offer valuable insights into how governments allocate resources to address environmental issues and the effectiveness of their financial strategies in implementing environmental policies. This approach enables a nuanced understanding of the interplay between policy decisions, financial commitments and environmental outcomes whilst also identifying gaps and areas for improvement in resource allocation, thereby contributing to the broader discourse on effective environmental governance and sustainable economic development.

Nonetheless, besides the set of indicators selected to evaluate governmental expenditure on combating climate change, assessing the direct relationship between climate change risks and economic growth necessitates a more focused approach. Consequently, we have chosen GDP per capita (real GDP) as the primary indicator. This measure provides a nuanced understanding of economic performance by accounting for inflation and population size, thereby offering a more precise depiction of the standard of living and economic health. By leveraging GDP per capita, we can more accurately analyse how climate change risks and related governmental expenditures influence economic growth at the individual level. The choice of GDP per capita as a key economic indicator is theoretically supported by its widespread use in macroeconomic analyses to reflect overall economic health and the well-being of citizens (Mankiw et al., 1992). Moreover, in environmental economics, GDP per capita is often used to explore the relationship between economic growth and environmental degradation, offering a benchmark to measure the trade-offs between growth and sustainability.

Further, as our primary objective is to examine the impact of climate change on economic growth, we used a multilinear regression model, which allows for the simultaneous assessment of multiple independent variables. This model is instrumental in isolating the specific effects of various climate change indicators on economic performance, offering nuanced insights into the complex interplay between environmental sustainability and economic dynamics. Through this methodological approach, we aim to contribute to the broader discourse on the economic implications of climate change and the efficacy of policy interventions.

Our empirical analysis leverages these indicators to quantify different climate scenarios’ economic costs and benefits, offering policymakers evidence-based insights. This comprehensive data set facilitates a robust examination of the effectiveness of EU environmental policies and their broader implications for sustainable economic development in the face of climate change.

The correlation coefficient and the multiple linear regression model were chosen to assess the impact of climate change indicators on economic growth. The correlation coefficient indicates the strength and direction of the relationship between variables. A significant correlation would present a meaningful association between two variables, suggesting that changes in one may influence the other. Therefore, we expect significant connections between our metrics, particularly between economic growth (expressed as GDP per capita) and the climate change indicators described above. We selected climate change indicators as independent variables due to their direct and significant impact on economic growth. Specifically, greenhouse gas emissions (CO₂), heating and cooling degree days, freight transport volume, industry production and final energy consumption were chosen based on their documented effects on both the environment and the economy, being considered as crucial in understanding how climate change influences economic performance.

Further, we adopted a multiple linear regression model to extend our statistical analysis. This statistical approach is particularly suited for examining the simultaneous effects of various independent variables on a single dependent variable, which in this case is economic growth. The selection of a multiple linear regression model allows for a comprehensive analysis of how different dimensions of climate change interact with economic performance, providing detailed insights into these complex relationships.

The multiple linear regression model is constructed with economic growth, measured as GDP per capita, as the dependent variable. The independent variables include a range of climate change indicators such as greenhouse gas emissions, cooling and heating degree days, freight transport volume, industry production and final energy consumption. The model estimates the coefficients for these independent variables, quantifying their individual and combined effects on economic growth. Positive coefficients indicate a direct relationship, suggesting that increases in the indicator are associated with higher economic growth, while negative coefficients imply an inverse relationship, indicating that increases in the indicator correspond to lower economic growth. Such an approach allows us to isolate the specific contributions of each climate change indicator, providing a detailed understanding of their relative importance and interactions (Pandis, 2016). This type of regression is a parametric test, which relies on data assumptions such as homogeneity of variance, normal distribution of the data set and the independence of the observations.

Thus, we conducted the multiple linear regression analysis to predict the variation in a dependent variable, specifically economic growth, in response to changes in independent variables – climate change indicators. This analysis was performed over a period of 12 years, with the equation expressed as follows [equation (1)]:

In the regression model, Y represents the dependent variable, while X (i.e. X₁, X₂…X_it) signifies the independent variables. Further, the β coefficient denotes the slope of the regression, whilst aaaa is the intercept or constant term, and ϵ accounts for the error term. The chosen countries are reflected by i (with i = 27), and the analysed period is denoted by t (with t = 1…12, precisely from 2010 until 2021). Consequently, when applying this framework to our study, the proposed regression model can be formulated as depicted by equation (2):

The subsequent phase of our analysis entailed evaluating whether governmental expenditure to mitigate climate change had a measurable impact on key climate change indicators. This assessment is critical to understanding the effectiveness of public policies and investments directed towards environmental protection. To this end, we used a MIMIC model as our primary statistical measure. The MIMIC model is particularly suitable for this type of analysis because it allows for the simultaneous examination of multiple indicators and their causal relationships with latent variables, providing a comprehensive framework for assessing the multifaceted impacts of government expenditure.

The MIMIC model facilitates the investigation of how governmental spending on various environmental initiatives influences a range of climate change indicators. These indicators include greenhouse gas emissions, net greenhouse gas emissions, final energy consumption, the volume of freight transport relative to GDP, production in industry, cooling and heating degree days and environmental tax revenues, among others. By integrating these indicators into the MIMIC model, we can observe both the direct and indirect effects of public expenditure on climate change mitigation. This method provides a robust statistical foundation for discerning the relationships between governmental actions and environmental outcomes, accounting for the complexity and interdependencies inherent in climate-related data.

Our model specification includes the independent variables representing governmental expenditures on environmental protection, pollution abatement, waste management and wastewater management, all expressed as a percentage of GDP. These variables are hypothesised to influence the latent variable of climate change mitigation effectiveness, which in turn affects the observed climate change indicators. The MIMIC model’s structural equations allow us to estimate the parameters and assess the strength and significance of these relationships. This approach helps identify which types of expenditures are most effective and quantify their impact on specific climate indicators.

In alignment with the methodology adopted in the application of multiple linear regression, the implementation of the MIMIC model encompassed an analysis incorporating data from all 27 EU member states over the period spanning from 2010 to 2021. This comprehensive approach enabled a thorough examination of various socioeconomic indicators and their potential causative factors, thereby facilitating a nuanced understanding of the complex dynamics influencing the outcomes under investigation. The MIMIC model thus provides a detailed framework for analysing how different forms of governmental expenditure affect climate change indicators, offering valuable insights into the effectiveness of EU-level environmental policies.

Equation (3) used in implementing the MIMIC model to analyse the governmental expenditures on environmental initiatives against climate change indicators whilst assessing the effectiveness of EU-level environmental policies is expressed as follows:

While widely used in the academic field, the MIMIC model faces significant criticism and limitations across academic research. Glover et al. (2001) critique its technical adequacy in detecting differential item functioning, highlighting challenges in accurately specifying relationships between latent variables and indicators, while Gibbons et al. (2012) raise concerns about using the MIMIC model for developing computerised adaptive tests, noting difficulties in modelling the underlying constructs accurately. Collectively, such critiques highlight the importance of cautious interpretation and rigorous validation when applying the MIMIC model, acknowledging its limitations in handling complex, dynamic data sets and assumptions inherent in its structural specification.

Our statistical analysis is structured into two distinct sections. Firstly, we assessed whether climate change risks influence economic growth using multiple linear regression. This approach allows us to examine the potential impacts of various climate change indicators on economic performance. Subsequently, aligning with the environmental framework established by the EU, we analysed whether expenditures targeted at mitigating climate change have effectively reduced these risks. This evaluation is conducted through the MIMIC model, supplemented by an analysis of the Green Growth Index. These methodologies enable a comprehensive assessment of the effectiveness of EU environmental policies in addressing climate change concerns and fostering sustainable economic development.

The primary focus of this research is to explore the relationship between climate change risks and their corresponding indicators on environmental growth. As outlined in the previous sections of this paper, we have used a multiple linear regression model to analyse this relationship comprehensively. The findings of this regression analysis are presented in Table 4 and Figure 2 below, offering a detailed examination of how various climate change risk factors, such as net greenhouse gas emissions, final energy consumption and other relevant variables, impact GDP per capita percentage growth. This analysis aims to explain the extent to which environmental and industrial factors contribute to economic growth in the context of climate change.

Following our initial assumptions, the statistical results of our analysis reveal a clear connection between climate change risks and economic growth. The R and R² demonstrated a significant model for describing the relationship between variables, with an R-value of 0.8880 and an R² value of 0.7880. The adjusted R² value is 0.7840, suggesting that approximately 78.40% of the variability in GDP per capita percentage growth is explained through the independent variables included in this model. In addition, the root mean squared error (RMSE) is substantially lower at 3.8350, indicating better predictive accuracy.

The analysis of variance table further supports the efficacy of the alternative model. The regression sum of squares is 16,587.06 with 6 degrees of freedom (df), resulting in a mean square of 2764.51. The residual sum of squares is 4,470.56 with 304 df, giving a mean square of 14.71. The total sum of squares is 21,057.62 with 310 df. The F-statistic is 187.99 with a p-value less than 0.001, indicating that the independent variables collectively have a significant effect on the dependent variable, thereby validating the model’s statistical significance.

The intercept for our model is 0.042, with a standard error of 0.020 and a t-value of 2.128, which is statistically significant with a p-value of 0.034. This suggests that when all predictors are zero, the GDP per capita percentage growth is 0.042%. Notably, the net greenhouse gas emissions percentage growth has an unstandardised coefficient of 1.535 with a standard error of 0.056 and a highly significant t-value of 27.587 (p < 0.001). This indicates that a 1% increase in net greenhouse gas emissions is associated with a 1.535% increase in GDP per capita percentage growth, holding other factors constant.

Other significant predictors include final energy consumption percentage growth, which has an unstandardised coefficient of 0.221 (p = 0.027), suggesting a positive relationship with GDP growth. Conversely, production in industry percentage growth has a negative association, with an unstandardised coefficient of −1.835 and a t-value of −7.929 (p < 0.001). Similarly, the cooling and heating degree days percentage growth has a negative impact, with an unstandardised coefficient of −0.228 (p = 0.006). However, the volume of freight transport percentage growth does not significantly affect GDP per capita percentage growth, as indicated by its unstandardised coefficient of −0.015 and a p-value of 0.877.

To evaluate the impact of governmental expenditure on climate change indicators, we used the MIMIC model. The results of our model can be found in Table 5 and Figure 3 below.

As predicted, the Chi-Square test results indicate a significant model fit, with the baseline model (χ2 = 1912.886, df = 39, p < 0.001) and the factor model (χ2 = 316.025, df = 29, p < 0.001) both demonstrating significant p-values, suggesting that the data used is proper for the related model. The parameter estimates section reveals the effects of different predictors, measured as expenditures on various environmental protections as percentages of GDP, on the latent variables, whilst the indicators section details how various economic and environmental indicators relate to the latent constructs in the model. Table 6 provides comprehensive statistical insights into the various predictors and indicators used in our analysis, elucidating their respective impacts on the latent variables within the MIMIC model framework.

The regression analysis demonstrates a substantial portion of the variance in GDP per capita percentage growth is explained by climate change indicators. Significant positive relationships are observed between net greenhouse gas emissions and final energy consumption, suggesting that these factors are closely tied to economic growth. However, production in industry and cooling and heating degree days show negative impacts, which can be attributed to resource depletion, increased energy costs and environmental degradation. These findings suggest that while certain climate change indicators can drive economic growth, others may present challenges due to their associated costs and inefficiencies. This aligns with the work of Jackson (2017), who highlights how industrial activities and energy consumption can contribute to environmental degradation, which in turn can negatively affect economic performance.

Nonetheless, even though several indicators (namely, production in industry, the volume of freight transport and cooling and heating degree days) seem to impact GDP per capita within this econometric model negatively. Such assessment could be, in turn, explained by several assumptions, which will be further explained.

Firstly, the negative impact of industry production on GDP per capita can be attributed to resource depletion and environmental degradation. High levels of industrial activity often imply significant pollution and resource depletion, which harm public health and reduce productivity. The United Nations Environment Programme (2011) also reflects upon how resource-intensive industries can lead to diminished economic output due to the environmental costs associated with industrial pollution. In addition, if outdated or inefficient industries drive industrial growth, it may incur higher operational costs and yield lower profits, thereby reducing overall economic output. Strict environmental regulations and compliance costs can further burden companies, reducing their profitability and contribution to GDP.

Further, cooling and heating degree days can negatively affect GDP per capita due to increased energy costs. Extreme temperature variations lead to higher energy consumption for cooling and heating, reducing disposable income and overall economic productivity. Furthermore, extreme temperatures can harm public health, increase health-care costs and lower labour productivity. This is supported by Zhao et al. (2023), who find that extreme temperature variations can increase energy costs and reduce economic productivity. The strain on infrastructure from temperature fluctuations can also raise maintenance costs, further hindering economic growth.

Finally, the volume of freight transport can negatively impact GDP per capita due to environmental and operational inefficiencies. Increased freight transport is associated with higher greenhouse gas emissions and pollution, leading to environmental degradation and health problems that detract from economic growth. High freight volumes can also cause traffic congestion and accelerate infrastructure wear and tear, increasing maintenance costs and reducing transport efficiency. Operational inefficiencies, such as delays and increased fuel consumption, further elevate expenses and diminish the positive impact on GDP.

However, in summary, the regression model effectively explains a substantial portion of the variance in GDP per capita percentage growth. Several predictors, including net greenhouse gas emissions, final energy consumption, production in industry and cooling and heating degree days, exhibit significant relationships with the dependent variable, underscoring their importance in understanding economic growth dynamics.

The MIMIC model offers valuable insights into the effectiveness of governmental expenditures in mitigating climate change. The significant impact of expenditures on environmental protection and the nuanced effects of pollution abatement underscore the complex nature of climate policy implementation. This is consistent with Goulder and Parry (2008), who highlight that targeted financial investments in environmental initiatives can lead to observable improvements in climate change indicators. The model reveals that financial investments in environmental initiatives do have a tangible effect on climate change indicators, validating the importance of targeted policy measures. The observed relationships between expenditures and climate indicators provide crucial information for policymakers, highlighting the need for continued investment and strategic allocation of resources to achieve sustainable economic and environmental outcomes. Aldy and Stavins (2012) argue that consistent and well-targeted financial commitments are essential for addressing climate change effectively and achieving desired environmental outcomes.

As evidenced by the chi-square test results, the MIMIC model effectively captures the significant impact of various expenditures on climate-related measures on the latent environmental and economic variables. The detailed parameter estimates for predictors such as expenditure on environmental protection, pollution abatement, waste management and wastewater management underscore the nuanced ways in which financial commitments to these areas influence broader economic and environmental outcomes. For instance, while expenditures on environmental protection demonstrate a positive, albeit marginally significant, effect on the latent variables, other expenditures, such as those on pollution abatement, show a more complex and occasionally negative relationship, highlighting the multifaceted nature of climate change mitigation efforts.

Furthermore, the analysis delves into economic and environmental performance indicators, revealing the substantial effects of growth in cooling and heating degree days, industrial production, energy consumption, greenhouse gas emissions and freight transport volumes. The significant positive relationship between final energy consumption and greenhouse gas emissions with the latent variables underscores the ongoing challenges in balancing economic growth with environmental sustainability, as reflected by the Intergovernmental Panel on Climate Change (2014), which highlights the need to balance economic development with efforts to reduce greenhouse gas emissions. These findings provide a critical understanding of how climate change impacts economic activities and, conversely, how economic activities contribute to climate change.

As such, the European Commission’s increased expenditure on mitigating climate change has been evaluated through climate change indicators. The consistent financial investment in environmental protection, pollution abatement, waste management and wastewater management appears to have a tangible effect on these indicators. For example, expenditures to reduce greenhouse gas emissions and enhance energy efficiency have likely contributed to observable improvements in related indicators. This relationship is vital for policymakers, as it validates allocating resources towards specific environmental initiatives and their subsequent impact on mitigating climate change.

Our study aimed to initially investigate the academic interest in climate change risks within the macroeconomic environment through a bibliometric analysis. Our findings indicate a notable increase in scholarly attention to this research area coinciding with the initiation of environmental policies under the European Green Deal by the European Commission. Despite this heightened interest at a global level, our results reveal a relative lack of comprehensive research specifically focused on this topic within the context of the EU. Nonetheless, as the European Green Deal represents a catalyst for environmental policy implementation, its implementation has sparked academic inquiry into climate change’s macroeconomic implications. However, our bibliometric analysis underscores a need for more extensive investigation into how climate change risks specifically impact the EU’s economic landscape. By identifying this research gap, our study contributes to the current understanding and underscores the importance of expanding scholarly efforts to comprehensively address and mitigate climate change risks within the EU’s macroeconomic framework.

To offer a holistic view of how climate change affects the macroeconomic environment, our study used two distinct econometric models: multiple linear regression and the MIMIC model. These models helped investigate the dual objectives of assessing the impact of climate change on economic growth and evaluating the effectiveness of environmental policies in mitigating climate change risks.

Our findings reveal nuanced insights into the relationship between climate change indicators and economic growth. Specifically, net greenhouse gas emissions and final energy consumption emerged as positive contributors to economic growth within our multiple linear regression model. Conversely, production in industry, cooling and heating degree days and volume of freight transport were identified as negative influences on economic growth, attributable to operational inefficiencies associated with these variables.

Moreover, the MIMIC model analysis demonstrated that increased environmental expenditure by EU countries has effectively reduced greenhouse gas emissions. However, this reduction has come at a cost, negatively impacting production in industry, which could be factually justified as the implementation of stringent environmental reporting measures has imposed additional operational costs on production plants and companies, thereby offsetting some of the economic benefits derived from emission reductions.

Our research contributes a complementary perspective to existing studies by incorporating a broader array of indicators and a comprehensive period in our econometric analyses. By using multiple linear regression and the MIMIC model, we integrated a diverse set of climate change indicators and economic variables to assess their collective impact on economic growth. This comprehensive approach not only enhances the breadth and depth of our findings but also situates our study within a broader context of literature that seeks to understand the intricate relationships between environmental sustainability and economic development. As such, our research contributes valuable perspectives and empirical evidence that can inform and complement ongoing discussions and future research endeavours in the field of environmental economics and policy evaluation.

Furthermore, our findings underscore the complex trade-offs inherent in environmental policy implementation within the context of economic growth. Correspondingly to the economic adjustments seen in fiscal policy sustainability, where structural changes in fiscal revenues and expenditures play a critical role in economic stability (Dima et al., 2009), the impact of environmental policies must also be considered in shaping long-term economic outcomes. While efforts to mitigate climate change through increased environmental expenditure have yielded positive outcomes in terms of emission reduction, they have concurrently posed challenges to industrial productivity and economic efficiency. This duality requires a balanced policy formulation approach that optimises environmental sustainability without compromising economic growth and competitiveness.

Building on the insights garnered from this study, future research could delve deeper into several avenues to expand upon and complement our findings. Firstly, further investigation could explore the specific mechanisms through which environmental policies impact industrial sectors differently, as indicated by our analysis. Understanding these mechanisms would provide policymakers with more targeted strategies to mitigate potential economic downturns while achieving environmental goals. Secondly, additional studies could track the evolving impact of climate change indicators on economic growth over extended time periods, considering how economic resilience and adaptive strategies might evolve in response to changing environmental conditions. In addition, comparative analyses across different regions or countries could elucidate how varying policy frameworks and socioeconomic contexts influence the effectiveness of environmental policies in mitigating climate change risks.

Nonetheless, interdisciplinary research that integrates socioeconomic factors with environmental variables could enhance our understanding of the broader socioeconomic implications of climate change mitigation efforts, offering comprehensive insights into sustainable development pathways. By addressing these research gaps, future studies can contribute to advancing theoretical understanding and practical approaches to addressing the complex challenges of climate change and environmental sustainability.

This work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitalization, the project with the title “Economics and Policy Options for Climate Change Risk and Global Environmental Governance” (CF 193/28.11.2022, Funding Contract no. 760078/23.05.2023), within Romania’s National Recovery and Resilience Plan (PNRR) – Pillar III, Component C9, Investment I8 (PNRR/2022/C9/MCID/I8) – Development of a program to attract highly specialised human resources from abroad in research, development and innovation activities.

Economics and policy options for climate change risk and global environmental governance, CF 193/28.11.2022; Funding Contract no. 760078/2.