The Analysis of Goals, Results, and Trends in Global Climate Policy Through the Lens of Regulatory Documents and Macroeconomics

Abstract

The issue of improving the effectiveness of international climate policy, one of the main goals of which is to reduce greenhouse gas (GHG) emissions, poses a critical and acute challenge for the global economic system. At every COP conference and in every IPCC report, it is evident that current measures fall short. To address this gap, this study examines the structure and trends of global climate policy development through content analysis, PRISMA methodology, and correlation and regression analysis using censored Bayesian Tobit regression. The obtained results are supplemented with the LMDI (Logarithmic Mean Divisia Index) decomposition of the Kaya identity. The research covers 198 countries and 4241 documents spanning 1950 to 2023 that shape global climate policy. The results showed that (1) the success of climate goals varies depending on policy instruments, institutional conditions, and the time frame of analysis; (2) the greatest success in achieving climate targets was often observed in countries that adopted moderate, realistic, and institutionally supported targets; (3) in some cases, an overachievement of targets and GHG emissions reduction was a temporal observation or the result of economic decline; (4) in countries without officially declared targets, emissions also continued under similar growth trends, calling into question the effectiveness of current methods of setting up CO₂ emissions reduction targets. These findings provide a deeper understanding of the factors determining the effectiveness of climate policy. They highlight key barriers to achieving too ambitious emission reduction targets, which can lead to economic shocks and a subsequent increase in environmental impact. Ultimately, this research can contribute to the development of more realistic and effective decarbonization strategies.

1. Introduction

Due to changes in the global climate system since the 1950s, the United Nations (UN) Intergovernmental Panel on Climate Change (IPCC) has consistently warned that ongoing greenhouse gas (GHG) emissions will lead to “further warming and long-term changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts on people and ecosystems” [1]. These findings, alongside those of the UN Framework Convention on Climate Change (UNFCCC), have driven sustained global efforts (Figure 1), including negotiations under the UN Conference of the Parties (COP), to establish international agreements aimed at reducing GHG emissions.

The most significant international agreements are the Kyoto Protocol [2] and the Paris Agreement [3], which have been ratified by 194 countries as of 2024 [3]. Under the Kyoto Protocol, parties included in Annex I to the Convention committed to reduce GHG emissions by 5% below 1990 levels in the first period (2008–2012), and by 20% in the second period (2013–2020). This was the first time that mandatory GHG reduction targets were set for all industrialized countries, though notably, the U.S. did not ratify it.

Under the Paris Agreement, a target was set to limit temperature rise to 1.5 °C above pre-industrial levels [4], albeit without clear commitments from participating countries. That is, although it should cover about 88% of global emissions, achieving so-called net zero targets faces serious political [5], technological, and economic barriers [6]. For example, a major challenge is that the growth in anthropogenic GHG emissions is predominantly associated with the use of fossil energy that fueled the industrial revolution [7]. This has been a key factor contributing to the rapid development of the current high-income nations [8] and is still the subject of much debate for currently developing countries [9].

Despite the controversy and the continued growth of GHG emissions, it is now evident that climate change is a problem that poses a threat to the future of humanity [10,11]. To address this problem, significant efforts are being made to improve energy efficiency (EE), transform energy consumption patterns, promote clean energy, raise awareness of energy conservation, etc. [12]. The main tool to support and implement such measures is a set of regulations consisting of guidelines, mechanisms, and strategies, commonly referred to as climate policy [13].

Current climate policy is characterized by generally positive investment momentum (Figure 2). The global share of public and private investment has increased significantly over the past decade. Between 2011 and 2022, the public and private sectors provided a total of USD 7.4 trillion in climate finance, with the private sector contributing about half of this amount. However, while the private sector contribution is growing, it averaged 9.1% per year between 2011 and 2022, compared to 13.2% for the public sector [14,15] (Figure 2a). Public and private investments in “clean” technologies are distributed somewhat differently. For example, between 2013 to 2022, the private sector accounted for the largest share of investments in renewable energy (RE) globally [16]. In 2022, it accounted for 74%, providing about USD 366 billion. In the same year, public sector funding amounted to about USD 128 billion (26% of the total), bringing total investment to a record USD 495 billion [17] (Figure 2b).

Despite apparent progress in scaling up investments and emissions reduction targets, a serious disparity persists between the declared strategies and their actual implementation. This gap is particularly pronounced when comparing regional and industry-specific data, calling into question the sustainability of current climate trajectories and the feasibility of the Paris Agreement goals. Researchers [18,19] attribute this to the gap between policy discourse and reality, which is relatively poorly understood in the scientific literature.

Thus, there is a need for a deeper analysis of climate policy mechanisms, not only in terms of funding volumes, but also focusing on the institutional and regulatory aspects that determine the effectiveness of climate commitments. This study proposes an original approach to assessing the achievability of climate goals based on a comparative analysis of the structure of international climate policy, including both formal (legally binding) and political (non-binding) goals. The effectiveness of countries’ achievement of their climate goals can serve as a basis for a classification system and serve a deeper analysis of real macroeconomic trends. The scientific novelty of this study lies in the identification of systemic regularities and contradictions between declared goals and actual results, as well as in the proposal of a methodology that can serve as a tool for monitoring and adapting climate policy in the future. The results of the analysis may be of interest to both the academic community and practitioners, including government regulators, international organizations, and business decision-makers.

The paper is structured as follows: Section 2 reviews the relevant literature; Section 3 details the materials and methods; Section 4 presents the research findings; and the final section provides a discussion and conclusions.

2. Literature Review

2.1. The Essence of the Carbon Neutrality

The concept of carbon neutrality (net zero, or NZ, emissions) refers to achieving a balance between GHG emissions and their capture or compensation. This principle is formally established in major international agreements, including the Paris Agreement [5], and forms the foundation for most national climate strategies.

The scientific justification of the concept is based on two pillars: emission reduction (including the transition to RE sources [20], EE improvement, decarbonization of transport and industry [21]) and compensation of residual emissions through natural and technological solutions [22]. At the same time, the literature increasingly points out that over-reliance on offset mechanisms can undermine incentives to actually reduce emissions [23]. The strategic balance between these approaches is key to the sustainability of climate policy.

Despite the growing number of NZ strategies at various levels, IPCC [24] and IEA [25] emphasize the need for technological breakthroughs and setting aside investments in key areas such as 100% RE, large-scale hydrogen generation, carbon capture, utilization and storage, etc. [26]. However, implementation is hindered by structural challenges: high costs [27], heterogeneity of implementation, and uncertainty of effects, especially in nature-based solutions [28,29]. In addition, the scientific community points to risks of the “energy efficiency paradox” [30] and social injustice of the transition [31], which calls for rethinking transition trajectories. Figure 3 presents a map of arguments on key issues in the global economy’s transition to NZ.

The map presented does not pretend to cover 100% of the scientific literature, but it reflects the main issues under discussion, which on almost all fronts have predominantly negative connotations [32]. The challenges range from cases where energy efficiency improvements lead to an overall increase in energy consumption [33] to increasingly important issues related to equality and justice [34], or when innovative technologies aimed at reducing CO₂ emissions inadvertently cause problems in other areas [35].

Despite criticism, NZ policies are actively promoted by associations such as Greenpeace [36] and protest movements such as Extinction Rebellion [37] and Fridays for Future [38]. This has prompted many political groups and governments to change their strategies [39], for example under the Green New Deal [40]. Unfortunately, this is currently only leading to increased criticism of green policies.

2.2. The Essence of Climate Policy

Climate policy has been of great importance to politicians in recent years. This is due to both the winnability of this policy agenda (e.g., in 2020, candidates supporting the Green New Deal initiative received a higher percentage of votes [41], including for a global agenda) and the reality of the threat to the existence of ecosystems on which people depend. This second reason is driving a growing number of people to demand decisive action [42], expressed in a set of measures and strategies designed and implemented to mitigate and adapt to climate change, which can be collectively labeled as climate policy (CP). The definition given in this paper differs somewhat from those found in the scientific literature (

Table 1. Definitions of climate policy in the literature.

№	Definition	Source
1.	Climate policies are strategies and actions taken by governments and organizations to mitigate the effects of climate change and adapt to its impacts. They involve integrating climate considerations into a wide range of policy areas, from environmental protection to economic development.	[43]
2.	Climate policy encompasses the public and governmental measures taken to reduce GHG emissions and adapt to the impacts of climate change. It includes regulatory, economic, and technological strategies to ensure fostering sustainable development and resilience.	[44]
3.	Climate policy is a set of measures aimed at mitigating the effects of climate change and facilitating adaptation. This includes economic instruments such as carbon trading, regulatory frameworks, and support for RE technologies.	[45]
4.	Climate policy includes urgent and coordinated global action to reduce GHG emissions and prepare society to cope with the impacts of climate change. It emphasizes the integration of scientific evidence into policy-making process.	[46]
5.	Climate policies are adopted and/or mandated by government—often in conjunction with business and industry within one country or with other countries—to accelerate climate change mitigation and adaptation measures.	[47]
6.	Climate policies are the decisions that determine what actions will be taken to combat climate change. Like politics in any other field, they range from the global level, where countries negotiate with each other, to the national, urban or even rural level.	[48]
7.	Climate policy is a set of measures and strategies aimed at addressing and mitigating the effects of climate change on the energy sector. These policies aim to ensure reliable and sustainable energy supply, promote the transition to clean energy, increase EE, and support the development and deployment of RE technologies. Climate policy also includes preparing for and adapting to long-term changes in climate patterns and immediate shocks caused by extreme weather events.	[49]

). First, it should be recognized that climate policy can be of different levels (global, national, regional, corporate, etc.). Second, it can have a different scale and sectoral affiliation (industry-specific, sectoral, inter-sectoral, etc.).

All climate policies fall into two broad groups in terms of strategic objectives: (1) mitigation of climate change and (2) adaptation to that change. Financing mechanisms can also be roughly divided, but they tend to fall into one of these two areas. As early the beginning of the last century, the overemphasis on “climate change mitigation” has been noted [50], and the UNFCCC and COP adaptation agreements created a bias against investment in climate change adaptation. However, recent initiatives, including the Sharm El Sheikh Adaptation Agenda [51], emphasize the growing importance of adaptation measures. In practice, the imbalance between these areas still persists. For example, in the central Climate Policy Database [52], an analysis of the structure of issued climate policies showed that the target parameter, mitigation, is 91.9% for 6028 issued climate policies in 198 countries (

Table 2. Structure of climate policies by parameter according to the Climate Policy Database.

Status	Jurisdiction	Policy Objective	Mitigation Area	Sector	Policy Instrument
Ended (17.7%)	City (0.8%)	Adaptation (4.7%)	Energy efficiency (37.2%)	Agriculture and forestry (7.2%)	Barrier removal (1.3%)
In force (73.8%)	Country (94.5%)	Air pollution (1.3%)	Energy service demand reduction and resource efficiency (25.2%)	Buildings (14.3%)	Climate strategy (9.7%)
Planned (0.6%)	Subnational level (4.7%)	Economic development (0.9%)	Non-energy use (6.4%)	Electricity and heat (29.1%)	Economic instruments (6.1%)
Superseded (8.3%)		Energy access (0.2%)	Other low-carbon technologies and fuel switch (8.2%)	General (24.4%)	Information and education (6.5%)
Under review (0.1%)		Energy security (0.4%)	Renewables (23%)	Industry (9.4%)	Policy support (37.95%)
Draft (0.1%)		Food security (0.1%)		Transport (15.6%)	Regulatory Instruments (16%)
Unknown (0.4%)		Land use (0.6%)			Research & Development and Deployment (RD&D) (1.7%)
		Mitigation (91.9%)			Target (19%)
		Water (0.01%)			Voluntary approaches (1.7%)

).

The structure of the climate policies also shows that most of them (73.2%) are in the status of “active”, indicating a high level of activity and real-time action. However, when compared to the “completed” status, it can be assumed that more and more efforts and actions are directed towards the implementation of these policies rather than towards approaching the targets. In terms of target areas, a significant proportion of policies are directed towards energy efficiency (37.2%) and RE (23%). This reflects the priorities of the transition to a low-carbon economy.

According to United Nations Environment Programme (UNEP) 2023 report, global CP will reduce the projected growth of GHG emissions by 2030 from 16% to 3%, indicating a significant contribution of current measures [53]. Meanwhile, for example, in the Organization for Economic Cooperation and Development (OECD) and European Union (EU) countries, efforts have been sufficient to offset all economic growth and stabilize GHG emissions at 1999 level [54].

However, despite the positive overall figures, the effectiveness of CP varies greatly from country to country [55]. Case studies from Chile [56], France [57], Canada [58], and Australia [59] reveal how domestic conditions and natural factors frequently influence policy outcomes.

It is also important to realize that the entire content of the CP is related to GHG emissions; however, the term “greenhouse effect” itself was introduced by natural sciences. The analysis shows that the lack of monitoring, poor institutional coordination, and distortions in the transfer of knowledge from natural sciences to policy are major obstacles to sustainable implementation of the CP [60]. Figure 4 presents a conceptual framework for the scientific-to-policy transition of the climate challenge.

The impact of anthropogenic activities on climate has long been uncontroversial in the scientific community [5,61]. Today, it is believed that scientific consensus on the causes of global warming has been reached: the climate is changing as a result of human activities, especially through the use of fossil fuels and deforestation.

However, as the conceptual funnel in Figure 4 shows, the main difficulties arise not at the stage of scientific discovery, but at the stage of translating this knowledge into policy decisions. As one moves from scientific diagnosis to policy implementation, distortions, uncertainties, and conflicts of interest increase. These barriers are driven by both internal (elites’ priorities, capacity to act, political party pressures) and external factors (socio-economic conditions, global agenda, foreign pressures).

For example, at the “Public Awareness and Media” stage, perceptual distortions arise due to the popularization of simplified or distorted interpretations of climate threats. Further, subjective interests, lobbying, and institutional constraints come to the forefront in shaping policy decisions, leading to policy fragmentation and reduced effectiveness.

The issue of scientific support for climate policy is of particular importance in this context. Without a transparent and balanced system of expertise, there is an increased risk that priorities will be skewed towards populist or unilateral solutions. For example, excessive pressure on greening without taking into account the economic and technological consequences may lead not to a reduction but to a paradoxical increase in environmental risks. History knows examples where hasty abandonment of nuclear or gas-fired generation has led to a return to coal or inefficient, high-emissions bioenergy [35,62].

It is therefore crucial to maintain an interdisciplinary balance between environmental, economic and social sciences when designing climate policy. Thoughtless radicalization of environmental protection measures can lead to economic degradation and technological regression, pushing the industry back into an era of less clean technologies. Only a sustainable combination of scientific rigor, technological maturity, and socially oriented policies can ensure a fair and effective climate transition.

Thus, the essence of climate policy today can be defined as a set of strategies and measures aimed at mitigating and adapting to climate change, with an emphasis on cross-sectoral cooperation, integration of scientific evidence, and active engagement of different levels of governance. The presented data and their analysis confirm the importance of an integrated and balanced approach in the development and implementation of climate policy, which is crucial for effectively addressing global climate challenges.

3. Materials and Methods

3.1. Selection of Data, According to PRISMA

The Climate Policy Database [52], which covers 6028 climate policies in 198 countries, was used as the main data source. According to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, the process of selecting climate policies for subsequent analysis was carried out in four consecutive steps: identification, screening, eligibility, and inclusion in the study [63] (Figure 5).

At the identification stage, 1787 records were excluded due to the missing or incomplete data. In the subsequent screening phase, 4241 records were selected that passed the initial screening for structured and analyzable information. Of these, 3452 records were excluded based on jurisdictional level, policy status, and type of climate policy instrument used. Several records were excluded due to their association with city- or subnational-level initiatives, while others were in draft form or classified as having undetermined status. During the assessment of eligibility for inclusion in the preliminary list, 789 records were selected, of which 597 were categorized as Political (P)—non-binding policies and commitments—and 192 were categorized as Formal (F)—legally binding regulations documents and official acts.

The eligibility assessment was based on the existence of quantifiable objectives, relevance to the thematic focus of the study (i.e., GHG, RE, EE), relevance to the Paris Agreement framework, and the availability of associated country economic data. Records that did not contain clear objectives, dealt exclusively with supranational objectives, or lacked sufficient contextual information were excluded from the final dataset. In the end, 552 policies were selected for in-depth analysis, of which 383 were categorized as Political (P) and 169 as Formal (F) (Figure 5). These records formed the core empirical basis for studying the structural characteristics of climate policy instruments and conducting a quantitative assessment of the feasibility of achieving the stated climate goals.

3.2. Analyzed Data Structuring

All climate targets included in the post-PRISMA analysis were categorized into three areas—GHG, RE, EE—within the specified types of policies.

Table 3. Structure of areas and description of targets.

Area	Content	Target Description	Economic Indicator	Source
GHG	GHG emissions	Reducing/increasing GHG emissions	GHG emissions	[64]
	GHG emissions excluding LULUCF	Reducing GHG emissions excluding LULUCF	GHG emissions excluding LULUCF	[65]
	CO2 emissions	CO2 emissions reduction	CO2 emissions	[66]
	CO2 intensity of GDP	Decreasing/reducing/increasing CO2 intensity	CO2 intensity of GDP	[67]
	Methane emissions	Methane emissions reduction	Methane emissions	[68]
	GDP increasing	GDP increasing	GDP	[69]
RE	RE mix	RE in the energy mix	RE generation	[70]
	RE final consumption	RE share in total final consumption	RE % of total final energy consumption	[71]
	RE primary consumption	RE share in primary consumption	Share of primary energy consumption from RE	[72]
	RE electricity consumption	RE share in electricity generation	Share of electricity production from RE	[73]
EE	Final energy consumption	Reduction in final energy consumption	Final energy consumption	[74]
	Primary energy consumption	Reduction in primary energy consumption	Primary energy consumption	[75]
	Energy intensity of GDP	Energy intensity reduction	Primary energy consumption per unit of GDP	[76]
	Energy savings	Energy savings	Final energy consumption	[74]
	Nuclear energy generation	Nuclear energy generation growth	Nuclear energy generation	[77]

presents the structure of these areas and the description of targets—economic indicators to calculate the percentage of achievability of these goals.

All targets were converted to a single unit of measurement in each area. Some targets were presented in MW and GW. To convert to TWh, formulas with installed capacity utilization factor (ICUF) were used depending on the country where the target was set. There are 5 such countries in total; their capacity utilization factors were taken from [78] and the conversion to TWh was done using the following Formula (1):

$$Annual generation=Capacity·365 days·24 h·Capacity factor.$$

(1)

3.3. Macroeconomics Indicators

For further analyses, this paper uses various macroeconomic data as presented in

Table 4. Characterization of economic indicators.

Indicator	Variable	Measure	Source
Population	x1	people	[79]
GDP per capita PPP	x2	2021 USD	[80]
Primary energy consumption	x3	TWh	[81]
GHG emissions	x4	CO2 eq	[64]
GDP	x5	2021 USD	[69]
Per-capita GHG emissions	x6	CO2 eq	[82]
GHG emissions per GDP	x7	kg 2021 USD	[67]
GHG emissions per unit energy	x8	kg per kWh	[83]
GHG intensity of electricity	x9	gCO2/kWh	[84]
Electricity consumption	x10	TWh	[75]

.

The main objective is to examine the relationship between macroeconomic performance and the achievability of the stated targets for GHG, RE, and EE emissions under the Paris Agreement (Nationally Determined Contribution (NDC)) for a number of countries. This study used time series data for the period from 1990 to 2023 from the [85,86] databases.

Macroeconomic indicators were chosen because of their key role in analyzing the dynamics of GHG emissions, EE, and RE development in the context of sustainable economic growth. The relationship between GDP, primary energy consumption, and carbon intensity of production is the subject of active research in environmental economics [87,88]. According to an IEA report [89], GDP per capita at purchasing power parity (PPP) reflects a country’s level of economic development and has a significant impact on its ability to reduce CO₂ emissions during the transition to a low-carbon economy [90]. The inclusion of CO₂ emission factors per unit of GDP and per unit of energy consumed (CO₂/GDP and CO₂/kWh) allows assessment of the efficiency of economies in reducing their carbon footprint, which is in line with the methodological approaches used in the IPCC [5] and IRENA [91] reports. Thus, the selected indicators cover key aspects of economic activity, providing a comprehensive analysis of the achievability of climate goals set under the Paris Agreement.

The goal achievability was calculated using the following Formulas (2) and (3):

$$Achievement\text{\_}\%=\left({\displaystyle \frac{\frac{\left(X-Y\right)}{Y}·100}{Target}}\right)·100$$

(2)

$$Acctual\text{\_}Change\text{\_}\%={\displaystyle \frac{(X-Y)}{Y}}·100$$

(3)

where X is the actual value for the target indicator for the target year; Y is the value for the target indicator for the base year; and Target is the target indicator used for normalization.

If the unit of measurement of the target indicator is specified in %, when calculating Achievement_%, the relative change was calculated first, and then the result was normalized to the target value.

To conduct correlation and regression analysis, the dependent variable (y) was Achievement_%; the independent variables, i.e., factors (x_n), were macroeconomic indicators (Table 3), namely their compound average annual growth rate (CAGR) (Formula (4)).

$$CAGR={\left[{\displaystyle \frac{EV}{BV}}\right]}^{{\displaystyle \frac{1}{n}}}-1,$$

(4)

where $EV$ is the value at the end of the period; BV is the value at the beginning of the period; and n is the number of cumulative periods.

The use of CAGR has several important advantages over the use of purely macroeconomic indicators, and this can be substantiated through various theoretical and empirical studies. For example, the impact of seasonal variations in GDP or CO₂ emissions, different levels of economic development of countries, or short-term fluctuations. CAGR avoids these shortcomings, more fairly assesses changes between countries in dynamics, and smooths out random deviations [92,93,94].

3.4. Methodology

3.4.1. Correlation Analysis

For correlation analysis, Pearson’s correlation coefficient was used, which measures the linear relationship between two variables. Mathematically, the correlation coefficient for two variables X and Y is calculated by the Formula (5):

$$r_{XY}={\displaystyle \frac{\sum _{i=1}^n\left(X_i-\overline{X}\right)\left(Y_i-\overline{Y}\right)}{\sqrt{\sum _{i=1}^n{\left(X_i-\overline{X}\right)}^2\sum _{i=1}^n{\left(Y_i-\overline{Y}\right)}^2}}},$$

(5)

where $r_{XY}$ is the correlation coefficient between variables X and Y; $X_i \mathrm{a}\mathrm{n}\mathrm{d} Y_i$ are the values of the variables in the observations x; and $\overline{X} \mathrm{a}\mathrm{n}\mathrm{d} \overline{Y}$are their mean values.

A correlation matrix was computed for each subset of data corresponding to a particular domain (GHG, RE, EE), depending on the country’s income level (High Income, Middle Income, Low Income) and document type (Formal, Political) [95,96]. The matrix includes correlations between the Achievement_% variable and the corresponding macroeconomic indicators expressed through CAGR.

The correlation matrix for a set of variables $X_1, X_2,\dots , X_k$, is calculated as (Formula (6)):

$$R=\left[\begin{array}{c}\begin{array}{ccc}{r}_{X_1{X}_1}& r_{X_1{X}_2}& \begin{array}{cc}\dots & r_{X_1{X}_k}\end{array}\end{array}\\ \begin{array}{ccc}{r}_{X_2{X}_1}& r_{X_2{X}_2}& \begin{array}{cc}\dots & r_{X_2{X}_k}\end{array}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \begin{array}{cc}\ddots & ⋮\end{array}\end{array}\\ \begin{array}{ccc}{r}_{X_k{X}_1}& r_{X_k{X}_2}& \begin{array}{cc}\dots & r_{X_k{X}_k}\end{array}\end{array}\end{array}\right],$$

(6)

3.4.2. Bayesian Tobit Model

Censored regression in the form of a Bayesian Tobit model [97,98] was used to conduct the regression analysis because standard linear regression methods did not produce correct results due to the statistical insignificance of the obtained coefficients. A Bayesian Tobit model was chosen to account for the constraints on the dependent variable, Achievement_%, limited to the interval [−100,100], which corresponds to physical and economic constraints on the level of goal attainment. In addition, in this model, the regression coefficients β are estimated using the posterior distribution rather than the maximum likelihood method as in the classical Tobit model. Bayes’ theorem is as follows (Formula (7)):

$$P(\beta ,{\sigma }^2\left|Y, X\right)={\displaystyle \frac{P\left(Y\right|X,\beta ,{\sigma }^2\left)P\right(\beta ,{\sigma }^2)}{P\left(Y\right|X)}} ,$$

(7)

where $P(\beta ,{\sigma }^2\left|Y, X\right)$ is the likelihood (the same function as in the classical model); $P(\beta ,{\sigma }^2)$ is the posterior distribution; and $P\left(Y\right|X)$ is the normalization constant.

Mathematically, the Bayesian Tobit model is described as follows (Formula (8)):

$$y_i^{*}={\beta }_0+{\beta }_1{x}_{i1}+{\beta }_2{x}_{i2},\dots , {\beta }_k{x}_{ik}+{ϵ}_i ,$$

(8)

where $y_i^{*}$is the latent variable, which is represented in the model as a linear combination of independent variables; ${\beta }_0$ is the free term of the model; ${\beta }_1,{\beta }_2,\dots , {\beta }_k$are regression coefficients reflecting the contribution of each economic indicator; and ${ϵ}_i~N(0,{\sigma }^2)$ is the model error, normal distribution with zero mean, and standard deviation $\sigma$.

However, the following restrictions are imposed on the observed dependent variable $y_i$ (Formula (9)):

$$y_i=\left\{\begin{array}{c}0 if y_i^{*}\le 0\\ y_i^{*} if 0<y_i^{*}<U\\ U if y_i^{*}\ge U\end{array}\right.,$$

(9)

where $U$ is the upper bound for the dependent variable.

The posterior distribution obtained using Markov chains of Monte Carlo (MCMC) is used to estimate the parameters of the Bayesian Tobit model. The logarithm of the likelihood function for the Bayesian Tobit model subject to constraints is represented as follows (Formula (10)):

$$\log P\left(Y\right|X,\beta ,\sigma )={\sum }_{i=1}^n\log P_i\left(y_i\right|X_i,\beta ,\sigma ).$$

(10)

The iterative NUTS (No-U-Turn Sampler) estimation process is used to generate a sample from the posterior distribution [99]. At each step, new ${\beta }_t,{\sigma }_t$ are generated based on the previous values. The probability of adopting a new value is (Formula (11)):

$$r={\displaystyle \frac{P({\beta }_{t+1},{\sigma }_{t+1}|Y,X)}{P({\beta }_t,{\sigma }_t|Y,X)}} .$$

(11)

If $r>1$, a new value is accepted; if not, it is accepted with probability $r$. After several thousand iterations the distribution of parameters $\beta , \sigma$ is obtained.

3.5. Decomposition of LMDI Based on the Kaya Identity

The original Kaya identity [100] relates CO₂ emissions to population, energy consumption, and economic output, providing a quantitative framework for assessing the relative influence of major CO₂ emission factors on human activity. In the present study, the Kaya equation was modified to account for total GHG emissions, providing a more complete picture of climate impacts than the traditional focus on CO₂ alone (Formula (12)):

$$\mathrm{F}=\mathrm{P}\times \left({\displaystyle \raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}\right)\times \left({\displaystyle \raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$G$}\right.}\right)\times \left({\displaystyle \raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}\right),$$

(12)

where $F$is the GHG emissions; $P$is the population; $G$is the GDP per capita; and $E$is the energy consumption.

A more detailed examination of the factors underlying the GHG emission imbalance can be achieved through an in-depth decomposition analysis. A particularly suitable method for this purpose is the Logarithmic Mean Divisia Index (LMDI), which allows the total changes in selected indicators to be decomposed into additive components [101].

The total change in emissions can be represented as follows (Formula (13)):

$$∆F={∆F}_P+{∆F}_{{\displaystyle \raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}}+{∆F}_{{\displaystyle \raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$G$}\right.}}+{∆F}_{{\displaystyle \raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}},$$

(13)

where $∆F$is the change in emissions in Mt CO₂-eq; ${∆F}_P$ is the contribution of population in the change in emissions; ${∆F}_{{\displaystyle \raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}}$ is the contribution of change in GDP per capita; and ${∆F}_{{\displaystyle \raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}}$ is the contribution of change in carbon intensity.

Accordingly, the total change in emissions over time can be expressed as the sum of the contributions of the individual factors. Each component under LMDI is calculated as the logarithmic mean contribution between two time points, t (Formulas (14)–(17)).

$${∆F}_P={\displaystyle \frac{F_{t+1}-F_t}{\ln \left(F_{t+1}\right)-\ln \left(F_t\right)}}·ln\left({\displaystyle \frac{P_{t+1}}{P_t}}\right),$$

(14)

$${∆F}_{{\displaystyle \raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}}={\displaystyle \frac{F_{t+1}-F_t}{\ln \left(F_{t+1}\right)-\ln \left(F_t\right)}}·ln\left({\displaystyle \frac{{\raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}_{t+1}}{{\raisebox{1ex}{$G$}\!\left/ \!\raisebox{-1ex}{$P$}\right.}_t}}\right),$$

(15)

$${∆F}_{{\displaystyle \raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$G$}\right.}}={\displaystyle \frac{F_{t+1}-F_t}{\ln \left(F_{t+1}\right)-\ln \left(F_t\right)}}·ln\left({\displaystyle \frac{{\raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$G$}\right.}_{t+1}}{{\raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$G$}\right.}_t}}\right),$$

(16)

$${∆F}_{{\displaystyle \raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}}={\displaystyle \frac{F_{t+1}-F_t}{\ln \left(F_{t+1}\right)-\ln \left(F_t\right)}}·ln\left({\displaystyle \frac{{\raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}_{t+1}}{{\raisebox{1ex}{$F$}\!\left/ \!\raisebox{-1ex}{$E$}\right.}_t}}\right),$$

(17)

4. Results

4.1. Achievement of Targets

This section summarizes the results of the analyses. Figure 6 shows the geographical distribution of climate policies by type of instrument.

The breakdown of countries by climate policy instruments shows a significant preponderance of countries that have chosen politically uncertain and non-binding targets (Political)—175 countries—versus 63 countries that have chosen formal and legally binding targets (Formal). This reflects global differences in the level of economic development and the ability of countries to introduce binding climate measures.

To assess the progress of countries in achieving their climate-related targets, a series of visualizations was constructed to illustrate the average values of the achievement indicator (Achievement_%) for the three core domains of climate policy: GHG emission reductions (Figure A1), development of RE sources (Figure A2), and EE improvements (Figure A3). The analysis is based on a comparative assessment of these indicators across high-, middle-, and low-income country groups, according to the World Bank classification [102]. The graphs were constructed from aggregated data representing average achievement across all relevant subcategories in each domain (Table 3).

The vertical axis of each figure shows the countries in the respective income group and the horizontal axis shows the average percentage of target achievement in the specified domain. Color deposition ranges from red, denoting low or negative achievement, through gray, denoting moderate progress, to blue, denoting high or even excessive achievement exceeding 100%. Vertical reference lines are drawn at 0%, indicating no progress, and ±100%, indicating reaching or exceeding the target level.

In the GHG domain (Figure A1), there are significant differences in achievement across all income groups. High-income countries (Figure A1a) show the widest range of results, from well above target (e.g., Sweden, UK) to notably behind (e.g., the U.S., Canada). This heterogeneity highlights the diversity of national climate strategies within the same income range and supports the findings of recent studies [51,53] on the influence of domestic policy frameworks and economic structures on climate performance. Middle-income countries (Figure A1b), particularly large emerging economies such as China, India, and Indonesia, generally show lower levels of achievement. This trend is consistent with the literature on the “double burden” these countries face: the challenge of maintaining economic growth while reducing emissions [11]. Low-income countries (Figure A1c) tend to show moderate values, often close to zero or slightly positive. These results may reflect both their relatively low emissions and limited reporting capacity.

There is also considerable variation in RE domain (Figure A2). High-income countries (Figure A2a) show the highest average levels of achievement in RE development, in some cases exceeding 100%. This suggests strong regulatory support, a favorable investment climate, and developed energy markets, particularly in the EU [89]. Middle-income countries (Figure A2b) show a generally positive trajectory, though with less consistency. In these contexts, progress in deployment of RE is frequently contingent on external financing and international cooperation [103].

In the EE domain (Figure A3), differences between income groups are less pronounced. High-income countries (Figure A3a) generally report positive progress, although the average level of achievement remains lower compared to RE. This may be due to the fact that many of these countries have already achieved high baseline levels of EE, making further gains increasingly difficult [96]. Middle-income countries (Figure A3b) show moderate gains, with the most notable results in nations with strong industrial policies (e.g., China, Turkey).

The analysis of climate policies and the achievability of their targets in different countries allows us to draw a number of important conclusions that support several key hypotheses related to the process of formation and implementation of such policies.

First, the high proportion of policies that claim to achieve their targets indicates a clear tendency to set insufficiently ambitious or unrealistic targets. An important aspect is that many countries, especially emerging economies, set targets that are easily achieved without significant changes in policy or the economy. This allows governments to claim to have “achieved the targets” without expending significant effort or undertaking meaningful energy or industrial reforms. Such approaches can be seen in highly developed economies that are eager to showcase their successes, but in reality fail to introduce the necessary technologies or make real changes to reduce emissions. The International Monetary Fund (IMF) notes that despite efforts to reduce GHG emissions, current policies are insufficient to stabilize temperatures and prevent the worst effects of climate change [104]. UN reports have argued the same: current measures are not achieving the necessary rate of GHG emission reductions [103].

Second, several factors influence the failure to achieve stated targets in other countries. One is the lack of political will and determination on climate policy [105]. For many countries, especially emerging economies, the climate agenda is often not a priority, which reduces the motivation to meet ambitious targets. In addition, political instability or changes of power can also negatively affect the consistency and effectiveness of climate initiatives.

In addition, another important cause of the gap between stated targets and actual achievements is the problem of false claims or misreporting. In some countries, there may be insufficient monitoring and reporting mechanisms in place, allowing for misleading reporting of progress. In particular, this is due to a lack of transparent methods for measuring GHG emissions or insufficient data, making it difficult to accurately assess the actual impact of climate policies. Weak monitoring of climate commitments in a number of countries allows governments to claim success without actually changing the structure of their economies. For example, a study published on arXiv shows that much of oil and gas companies’ reporting of GHG emissions between 2010 and 2019 fails mathematical verification, calling into question the credibility of their climate intentions [106]. The Central Bank of Russia notes in its policy brief that the lack of incomparable data and the use of different standards of sustainability disclosure make it difficult to monitor and evaluate the effectiveness of climate strategies [107,108].

Thus, countries often underestimate the real costs of adaptation and GHG emission reductions, and fail to take into account the long-term effects of climate change on the economy and society. Such studies confirm that many countries set their targets based on optimistic scenarios without considering realistic constraints and impacts.

4.2. Correlation Analysis

In this subsection, a correlation analysis was conducted and matrices of pairwise correlation coefficients were constructed according to area, level of economic development of countries, and type of document (Figure 7). Based on the obtained correlation matrices, it is possible to identify key dependencies between the attainability of the goals and various economic factors.

4.2.1. GHG Direction

Correlation analysis for the GHG direction (Figure 7a–e) shows that in high-income countries with formal climate documents, the achievement of emission targets is virtually independent of macroeconomic factors; correlations with the target variable (y) are close to zero (Figure 7a). This indicates institutional resilience and a high degree of technological control over emissions [109]. In the same income group of countries, but within policy instruments, moderate negative correlations with GDP growth (x₅), emissions per capita (x₆), and emissions per unit of GDP (x₇) appear, indicating greater sensitivity to economic dynamics (Figure 7b).

In middle-income countries with formal targets, their achievement is positively correlated with most macroeconomic indicators, including GDP per capita (x₂), energy consumption (x₃), total GDP (x₅), and emissions per capita (x₆) (Figure 7c). This implies that targets are often consistent with, rather than constraining, emission growth. In contrast, policy documents in the same group show a weak or negative correlation with targets (Figure 7d), suggesting limited alignment between political declarations and actual economic conditions.

In low-income countries (data are only available for policy documents), correlations are unstable: negative for intensity indicators (x₇–x₉) and positive for GDP per capita (x₂) (Figure 7e). This reflects weak institutional capacity and high volatility, where target achievement is largely disconnected from macroeconomic realities.

4.2.2. RE Direction

In high-income countries with formal climate documents, the achievement of RE targets (Figure 7f–i) is weakly correlated with macroeconomic indicators. The strongest positive correlations are observed with x₅ and x₃, indicating that RE goals are implemented in the context of stable economic growth (Figure 7f). In policy papers, correlations increase slightly with population size (x₁), but remain generally weak with respect to other factors (Figure 7g).

In middle-income countries with formal documents (Figure 7h), there are strong positive correlations between target achievement and almost all economic variables, particularly x₂, x₃, and x₅. This suggests that RE targets in these countries are closely related to economic growth rather than reflecting fossil energy divestment [110]. Under political documents, correlations are weak or absent, indicating the declarative nature of such targets (Figure 7i).

4.2.3. EE Direction

In high-income countries with formal documents, the achievement of EE targets (Figure 7j–m) shows weak correlation with macroeconomic factors. Moderate positive correlations are observed with x₅ and electricity consumption (x₁₀), while negative correlations are observed with emission intensity indicators x₆, x₇, reflecting the impact of technological progress and institutional improvements (Figure 7j). In policy documents of the same group, correlations are stronger and extend to demographic and energy variables, indicating that such targets are reactive rather than directive (Figure 7k).

In middle-income countries with formal documents, the achievement of EE target is strongly correlated with GDP per capita (x₂ ≈ 0.95), x₃, x₅, and x₁₀ (Figure 7l). The policy documents show a similar pattern, albeit with slightly weaker correlations (Figure 7m). This confirms that EE targets in these countries primarily reflect existing economic growth trajectories rather than determining them. In contrast to the GHG and RE directions, EE targets in middle-income countries are integrated into macroeconomic dynamics, even when defined in policy terms.

The analysis of the three climate policy directions—GHG, RE, and EE—reveals fundamental differences in their dependence on macroeconomic factors and the type of climate instruments [111]. In the GHG domain, the achievement of targets is most sensitive to the quality and feasibility of strategies: only countries with realistic, institutionally supported formal documents show consistent emission reductions. In RE, targets in middle-income countries are closely linked to economic growth, especially when formal instruments are in place, while in high-income countries, their achievement is driven by technology rather than macroeconomic performance. EE is the most stable and economically integrated domain, where even policy targets are correlated with energy consumption [112] and GDP growth.

In all directions, formal documents are much more likely to reflect actual eco-nomic processes and produce predictable outcomes, whereas policy documents often fail to match macroeconomic dynamics. The effectiveness of climate policy is deter-mined not by the existence of targets, but also by their realism, institutional support, and relevance to the economic context of the country.

4.3. Regression Analysis

Regression analysis by OLS (Ordinary Least Squares) method did not give statistically significant results, so further analysis was performed on the basis of censored Tobit model. Before that, all the studied macroeconomic indicators were analyzed for multicollinearity using the VIF criterion (

Table 5. Results of multicollinearity analysis.

Variable	VIF
y	3.675398
x1	4.138842
x2	11.714378
x3	1.744677
x4	680.530770
x5	1198.295191
x6	3.412330
x7	1461.098623
x8	1.087580
x9	5.416158
x10	7.634015

).

Since x₄, x₅, and x₇ greatly exceeded the acceptable value of 10, they were not included for further analysis.

Several regression approaches were sequentially tested during the course of the analysis. Initially, a standard linear regression model was applied; however, it demonstrated poor model fit: the estimated coefficients lacked statistical significance, and the information criteria indicated overfitting and model instability. Furthermore, linear regression fails to account for the natural censoring of the dependent variable, namely, the percentage of target achievement (Achievement_%).

To address the limited distributional properties of the dependent variable, the classical Tobit model based on maximum likelihood estimation was employed [97,98]. Nevertheless, this approach also yielded unsatisfactory results. In the presence of small sample sizes across certain country groups and target categories, as well as pronounced multicollinearity among macroeconomic predictors, the model exhibited estimation instability and high sensitivity to parameter specifications. In several cases, the estimation failed to converge or produced inflated standard errors, thereby undermining the reliability of the results.

Given these limitations, a Bayesian Tobit model was selected as a more robust alternative to the classical specification. This model extends the traditional framework by incorporating prior distributions for the parameters and estimating the full posterior uncertainty. The Bayesian approach improves model stability under multicollinearity and small-sample conditions, which is particularly relevant when analyzing heterogeneous international datasets [113]. The results obtained using the Bayesian Tobit model (

Table 6. Estimates of Bayesian Tobit regression coefficients for different income groups of countries.

Parameter	GHG_High Income	GHG_Middle Income	GHG_Low Income	RE_High Income	RE_Middle Income	EE_High Income	EE_Middle Income
	Estimate	Estimate	Estimate	Estimate	Estimate	Estimate	Estimate
y	6.388	3.95	5.609	2.269	7.498	4.708	0.589
x1	3.119	3.123	14.755	3.307	7.084	3.51	0.306
x2	3.326	5.542	−8.144	2.927	21.15	8.577	3.164
x3	−5.419	−0.364	−2.146	2.223	8.853	−1.813	1.31
x6	−12.508	−0.967	−18.019	−1.725	2.228	−18.422	0.878
x8	−0.223	−4.805	−1.983	−2.669	−3.382	4.955	−0.793
x9	−10.51	−0.169	6.219	−5.272	−10.526	−9.337	−2.588
x10	2.408	5.21	1.011	5.355	19.316	5.024	3.525
Sigma	143.196	141.836	34.318	1222.178	261.625	133.886	1874.369
Log_Likelihood	−4262.44	−2583.32	−2165.35	−7259.76	−4483.5	–	–
WAIC	126.54	157.4	31.1	91.62	94.81	–	–
BIC	8560.26	5207.9	4350.46	14,547.35	8999.74	–	–
R2	0.39	0.49	0.55	0.06	0.2	0.15	0.21

) exhibit more stable values of information criteria and enable the derivation of substantive probabilistic inferences from posterior distributions, thereby enhancing both the validity and interpretability of the econometric analysis.

The Bayesian Tobit model presented in Table 6 in the form of regression coefficient estimates $\beta$ and likelihood metrics; the Watanabe–Akaike information criterion (WAIC); the Bayesian Information Criterion (BIC) and the coefficient of determination (R²), enabled the identification of key macroeconomic determinants influencing the achievement of climate-related targets, accounting for the censored nature of the dependent variable and the limited sample size across several countries. The most robust and interpretable results were obtained for targets related to the reduction of GHG emissions, particularly within high- and middle-income country groups. Corresponding values of WAIC and BIC confirm the superior model performance in this direction compared to models constructed for RE and EE, which exhibit substantially lower explanatory power and weaker associations with macroeconomic indicators due to the predominance of “political” documents.

The implementation of the Bayesian Tobit framework revealed distinct asymmetries in the effectiveness of macroeconomic drivers across climate policy domains and income-level groupings. The analysis highlights that progress in reducing GHG emissions is more systematically linked to structural economic indicators, whereas the advancement of RE and EE remains weakly embedded in macro-level dynamics. These findings emphasize the necessity of differentiated policy design that accounts not only for resource constraints but also for institutional and non-economic factors shaping climate outcomes. Moreover, the demonstrated capacity of the Bayesian Tobit model to handle data sparsity and censoring positions it as a valuable tool for future empirical research at the intersection of macroeconomics and environmental policy, especially in the context of globally uneven data availability and policy implementation capacity.

4.4. LMDI Decomposition of the Kaya Identity

The results obtained from the analysis of climate targets and the Bayesian Tobit model were subsequently used as a classifier for a deeper decomposition of the Kaya identity. The aim of this approach is to identify the relationship between the actual GHG emission trends of different groups of countries and their respective policy agendas. The set of indicators presented in

Table A1. Summary table of indicators for deep decomposition of Kaya identity.

Indicators	1. Significantly Exceeded (>100%)			2. Achieved (0–100%)			3. Not Achieved (0–−100%)			4. Fell Substantially Behind (< −100%)			5. No Targets
	High Income	Middle Income	Low Income	High Income	Middle Income	Low Income	High Income	Middle Income	Low Income	High Income	Middle Income	Low Income	High Income	Middle Income	Low Income
Aims of GHG emissions reduction
Number of countries	15	10	1	11	25	3	12	28	12	9	14	1	9	19	6
Total emissions in 2000, Mt CO2e (% of global)	3871.5 9.67	5617.3 14.02	33.3 0.08	10,434.8 26.05	8329.6 20.8	77.01 0.19	1769.5 4.42	4673.6 11.67	588.2 1.47	328.4 0.82	1889.04 4.72	123.5 0.31	377.6 0.94	1496.1 3.74	445.5 1.11
Total emissions in 2022, Mt CO2e (% of global)	2885.1 5.42	14,299.9 26.88	31.9 0.06	8885.5 16.7	9587.5 18.02	62.3 0.12	2587.7 4.86	7352.3 13.82	1257.5 2.36	273.9 0.51	3001.5 5.64	53.8 0.1	393.2 0.74	2064.8 3.88	457.1 0.86
Change in GHG emissions from 2000 to 2022:
-in Mt CO2e	−986.4	8682.6	−1.4	−1549.3	1257.9	−14.7	818.1	2678.6	669.2	−54.5	1112.5	−69.7	15.7	568.6	115.8
-in %	−25.48	154.57	−4.27	−14.85	15.10	−19.09	46.23	57.32	113.77	−16.59	58.89	−56.42	4.16	38.01	2.60
Aims of RE implementation
Number of countries	20	17	0	10	9	0	1	8	0	1	4	0	24	58	23
Total electricity consumption in 2000, TWh (% of global)	3578.88 24.46	3644.08 24.91	0	498.85 3.41	659.1 4.51	0	14.68 0.1	169.05 1.16	0	38.64 0.26	47.3 0.32	0	5299.66 36.23	614.36 4.2	64.47 0.44
Total electricity consumption in 2022, TWh (% of global)	4158.01 14.79	14,012.9 49.84	0	566.76 2.02	1440.62 5.12	0	18.81 0.07	409.84 1.46	0	67.75 0.24	104.55 0.37	0	6276.08 22.32	940.07 3.34	118.91 0.42
Total RE consumption in 2000, TWh (% of global)	432,542 17.12	376,261 14.9	0	178,972 7.09	790,952 31.31	0	47 0.002	743 0.03	0	24,949 0.99	2221 0.09	0	660,122 26.13	59,017 2.34	180 0.01
Total RE consumption in 2022, TWh (% of global)	3,013,529 17.27	7,182,474 41.16	0	785,020 4.5	2200 × 103 12.61	0	12,680 0.07	35,441 0.2	0	48,906 0.28	13,174 0.08	0	3,740,242 21.43	415,657 2.38	3569 0.02
Change in RE consumption from 2000 to 2022:
-in TWh	2,580,987	6,806,213	0	606,048	1409 × 103	0	12,633	34,698	0	23,957	10,953	0	3,080,120	356,640	3389
-in %	596.70	1,808.91	N/A	338.63	178.17	N/A	26,878.72	4,669.99	N/A	96.02	493.16	N/A	466.60	604.30	1882.78
Aims of EE improvement
Number of countries	3	3	0	7	8	0	4	2	0	1	3	0	41	80	23
Total primary energy consumption in 2000, TWh (% of global)	5274.42 4.87	20,177.95 18.64	0.00	6703.45 6.19	7632.76 7.05	0.00	1830.98 1.69	1216.23 1.12	0.00	6242.48 5.77	2989.26 2.76	0.00	45 × 103 41.85	10 × 103 9.38	728.33 0.67
Total primary energy consumption in 2022, TWh (% of global)	4560.79 2.76	55,016.55 33.31	0.00	5689.98 3.45	14,912.9 9.03	0.00	2177.03 1.32	1367.73 0.83	0.00	4956.2 3	4368.8 2.65	0.00	51 × 103 30.73	21 × 103 12.46	769.1 0.47
Total GDP in 2000, Mln int-USD_2011 (% of global)	3,430,132 5.8	8,638,040 14.6	0	49,486 × 103 8.37	4642 × 103 7.85	0	1057 × 103 1.79	372,380 0.63	0	4210 × 103 7.12	1869 × 103 3.16	0	21,989 × 109 37.17	7118 × 109 12.03	883 × 109 1.49
Total GDP in 2022, Mln int-USD_2011 (% of global)	4,790,925 3.72	34,197,530 26.53	0	6511 × 103 5.05	14,763 × 103 11.45	0	1897 × 103 1.47	752 × 103 0.58	0	4774 × 103 3.7	3605 × 103 2.8	0	37,254 × 109 28.9	19,010 × 109 14.75	1359 × 109 1.05
Total population in 2000, Mln (% of global)	109.4 1.8	1632.4 26.83	0	162.9 2.68	1264.8 20.78	0	33.8 0.56	49.2 0.81	0	127.02 2.09	203.04 3.34	0	627.5 10.31	1502.3 24.69	373 6.13
Total population in 2022, Mln (% of global)	124.6 1.58	1849.6 23.38	0	180.9 2.29	1650.9 20.87	0	41.1 0.52	64.2 0.81	0	125 1.58	248.3 3.14	0	744.7 9.41	2196.7 27.77	684.6 8.65
Energy intensity of GDP in 2000, TWh/USD	5.03 × 10−9	7.67 × 10−9	0	1.05 × 10−8	2.25 × 10−8	0	6.38 × 10−9	5.22 × 10−9	0	1.48 × 10−9	9.11 × 10−9	0	9.97 × 10−8	1.34 × 10−7	2.09 × 10−8
Energy intensity of GDP in 2022, TWh/USD	3.14 × 10−9	4.57 × 10−9	0	7.02 × 10−9	1.16 × 10−8	0	3.89 × 10−9	2.86 × 10−9	0	1.04 × 10−9	5.08 × 10−9	0	6.8182 × 10−8	1.10 × 10−7	1.7 × 10−8
Energy consumption per capita in 2000, kWh/person	164,059.89	63,897.39	0	316,772.64	148,130.01	0	155 × 103	40 × 103	0	49 × 103	45 × 103	0	2508 × 103	643 × 103	40 × 103
Energy consumption per capita in 2022, kWh/person	127,952.80	101,002.47	0	261,273.95	176,685.97	0	150 × 103	37 × 103	0	40 × 103	53 × 103	0	2596 × 103	891 × 103	27 × 103
Change in energy efficiency from 2000 to 2022:
-energy intensity of GDP, %	−37.70	−40.42	N/A	−33.05	−48.32	N/A	−38.97	−45.22	N/A	−30.00	−44.24	N/A	−31.63	−17.76	−17.04
-energy consumption per capita, %	−22.01	58.07	N/A	−17.52	19.28	N/A	−3.31	−7.99	N/A	−18.12	18.36	N/A	3.51	38.51	−33.53

is an integration of the results of the policy document analysis with the main components of the Kaya identity framework.

The table categorizes countries into five distinct groups based on the degree to which the targets are achieved: (1) Significantly exceeded (>100%), (2) Achieved (0–100%), (3) Not achieved (0% to −100%), (4) Significantly lagging behind (<−100%), and (5) No targets. For each group, the table presents aggregated statistics on the number of countries; total GHG emissions in the base (2000) and end (2022) years; and their share of global emissions by income level (high, middle, low income) and policy direction (GHG, RE, EE).

This classification serves a dual purpose: it not only captures aspects of the distribution of progress across income groups, but also links these trends to macroeconomic and demographic indicators embedded in the Kaya identity. The data show that countries with significantly exceeding and successfully meeting target groups (Groups 1 and 2) are mostly high-income or middle-income economies, and together account for the largest share of baseline global emissions. In contrast, the categories of under-achieving and missing targets (Groups 4 and 5) are mainly represented by low-income countries which, although contributing less to global emissions in absolute terms, face systemic challenges in institutional capacity, energy access, and data transparency.

The trends outlined in Table A1 highlight a clear asymmetry between ambition and capacity across income groups. These distinctions underscore the rationale for employing differentiated decomposition in the subsequent LMDI analysis (Figure A4, Figure A5 and Figure A6), which seeks to quantify the relative influence of Kaya identity factors in shaping GHG emission trajectories within each target-achievement cluster.

4.4.1. GHG Direction

Figure A4 presents the results of LMDI decomposition of aggregate changes in GHG emissions based on the Kaya identity. The graphs are constructed to show the contribution of the four main components—population growth, GDP per capita, energy intensity, and carbon intensity—to emission trends over the period 2000–2021. The columns correspond to the five country groups, categorized by their level of target attainment, and the rows represent the individual Kaya factors. This structure allows for a detailed assessment of how each factor independently contributes to the overall emission dynamics, consistent with the additive nature of the identity.

Population growth (first row) has a sustained positive influence on GHG emissions in all country groups. Changes in population size led to an increase in GHG per 100 million emissions in middle-income countries. In other groups, the figure is smaller, around 25 million. These patterns confirm the role of demographic pressure as a key driver of emissions growth in emerging economies.

The contribution of GDP per capita (second row) is also positive and significant, especially in Groups 2–4. This reflects a direct link between economic growth and emissions increase [114]. In contrast, high-income countries in Group 1 show a trajectory with a clearer decoupling: despite growth in GDP per capita, total emissions remain stable or decline due to compensatory effects from other factors.

Energy intensity (third row) has a negative impact in most cases, indicating that improvements in EE have led to lower emissions. This is especially evident in Groups 1 and 2, where a steady downward trend is observed. However, in Group 4, the energy intensity component shows significant volatility, particularly in low-income countries. These fluctuations may signal inconsistent implementation of energy policies or structural shifts in the economic base, such as increased reliance on industry or fossil-fuel-intensive sectors. In Group 5 (countries with no targets), the contribution of energy intensity is similarly volatile, with marked short-term spikes, especially in the 2010s, suggesting a lack of institutional coherence or long-term planning.

The fourth row, carbon intensity of energy, also shows a strong negative contribution in Groups 1 and 2, especially in high-income countries. This reflects a gradual shift to less carbon-intensive energy sources, such as natural gas and RE. While the downward trend is also observed in other groups, the dynamics here are more gradual and less stable. for example, in Group 4, short-term fluctuations (especially in Panel 3) indicate interruptions in energy supply or a temporary return to carbon-intensive fuels. Group 5 shows a similar downward trend, yet with high volatility, particularly in low-income countries, further illustrating the fragmented and reactive nature of energy transitions in these contexts. Countries without established climate targets (Group 5) exhibit markedly higher instability across all components of the Kaya identity, underscoring the critical role of formal climate policy in ensuring a consistent emissions trajectory. In contrast to countries with an active regulatory agenda, where emissions dynamics tend to be coordinated and predictable, the lack of a policy framework in Group 5 leads to fragmented, reactive, and often unstable patterns.

Moreover, whether or not a country has adopted formal climate policy documents, emissions continue to rise. This suggests that having stated targets alone does not guarantee emission reductions; the crucial factor is how well these targets are integrated into economic and energy policy frameworks.

4.4.2. RE Direction

The LMDI decomposition graphs for the RE direction (Figure A5) show more stable and homogeneous trends compared to the GHG emissions reduction graphs.

The impact of population growth remains positive in all groups, with the most pronounced effect in middle-income countries, similar to the area of GHG emissions.

GDP per capita growth also makes a significant and positive contribution in all groups, especially among those that have fully or partially achieved their targets.

In contrast, changes in energy intensity show a clear and consistent downward trend in all groups: EE has steadily improved over the observed period, leading to emission reductions, especially in countries that exceeded their targets (Group 1) and even in countries without targets (Group 5). The most stable and significant contribution to emission reductions stems from reducing the carbon intensity of energy. This effect is particularly strong in Groups 1 and 2, but even in Group 5 a steady decarbonization trend is noticeable.

Thus, the RE decomposition shows that the key drivers of emission reductions—improving EE and reducing carbon intensity—operate consistently and effectively, regardless of the degree of policy formalization. This contrasts with the GHG domain, where the availability and quality of policy targets play a crucial role in shaping emission trajectories.

4.4.3. EE Direction

The LMDI decomposition graphs in EE (Figure A6) show clear and systematic differences between country income levels and target achievement groups.

Population growth and GDP per capita consistently increase emissions in all groups, with the strongest impact in middle-income countries. These effects are stable and predictable over time.

Energy intensity shows the largest contribution to emissions reductions. Except for low-income countries, all groups show sustained and significant reductions in energy intensity. This effect is particularly pronounced in countries that exceeded or achieved their targets (Groups 1 and 2). Even in Group 5, a consistent downward trend is visible—across all income levels—indicating that technological improvements are taking place independently of formal policy frameworks.

Carbon intensity of energy also contributes negatively to emissions, but to a lesser extent than energy intensity. The downward trend is observed in almost all groups and remains moderately stable over time.

In contrast to the GHG direction, where the influence of energy and carbon intensity was unstable in several groups, EE shows more consistency in the contribution of the factors. Compared to RE, where decarbonization plays a leading role, emission reductions in EE are primarily due to energy efficiency improvements. In RE, emissions reductions are more associated with the shift in energy sources, while in EE, it results from technological modernization [115] and reduction of energy demand per unit of GDP.

4.4.4. Summary

The final comparative graph (Figure 8), showing emissions trends (as a percentage of the 2000 baseline) in GHG, RE, and EE directions for the five categorized country groups, serves as a comprehensive summary of the LMDI decomposition based on the findings of the Kaya identity. It illustrates how different levels of political ambition and target achievement are reflected in actual emissions trajectories.

In the GHG domain, fundamentally different scenarios emerge. The most paradoxical is the case when countries have significantly exceeded their targets (green line). Despite formally surpassing their goals, their emissions increased by more than 80% over the period. This suggests that the targets were either poorly defined or deliberately unambitious, allowing these countries to claim “success” without altering their emissions trajectory. This phenomenon of “target formalism” can undermine the credibility and effectiveness of the international climate architecture. Emissions also rose steadily in countries that failed to meet their targets (yellow line), reflecting a clear mismatch between stated intentions and actual policy implementation.

In contrast, the only group showing a consistent decline in emissions consists of countries that met their targets (blue line), with emissions falling below the 2000 baseline. This supports the hypothesis that moderate, realistic, and institutionally supported goals can lead to tangible emission reductions. At the same time, in countries without formal targets (red line), emissions continued to rise, but in many cases not as sharply as in countries with formally inflated but strategically shallow targets. This means that, paradoxically, lack of planning may in some cases be less detrimental than the presence of poorly formulated targets.

In the RE and EE directions, the trends are less striking but structurally similar: the most meaningful progress is seen in countries that met or significantly exceeded their targets, especially in the area of energy efficiency. However, unlike the GHG direction, even countries without formal targets are showing moderately positive dynamics—probably due to global technological shifts such as the declining cost of renewables and the increasing digitalization of energy systems.

This supports the hypothesis that moderate, realistic, and institutionally sound targets are more likely to lead to actual emissions reduction. At the same time, the example of countries such as China and Russia demonstrate that the adopting less ambitious or “unattractive” targets does not necessarily mean greater effectiveness in implementing climate policy [97,105,115]. On the contrary, the international community’s tendency to focus disproportionately on the formal ambition of climate targets without assessing their feasibility or the existence of enforcement mechanisms leads to both methodological and political distortions. In this context, criticizing countries that adopt restrained or pragmatic targets is misguided. The mere fact of setting targets, regardless of their stated ambition, does not correlate directly with real emission reductions and should not be assessed in isolation from national context, institutional capacity, and quality of implementation.

Beyond political analysis and emissions accounting, effective climate action must be viewed as part of a broader development trajectory. The final visualization (Figure A7), which illustrates countries’ evolution by total GDP and GDP per capita from 1960 to 2020, reinforces the core principle of the Kaya identity: emissions are not an isolated outcome, but a structural byproduct of demographic growth, economic activity, energy demand, and technological intensity.

Figure A7 clearly shows that countries follow different development paths: high-income industrial economies have achieved both high income levels and total GDP growth while maintaining the institutional capacity to implement climate policy. In contrast, low- and middle-income countries face a dual challenge: pursuing rapid economic growth while attempting to manage emissions. This visual dynamic underscores that climate targets must be realistic, proportionate to economic potential, and backed by institutional mechanisms, rather than being merely ambitious on paper.

Thus, meaningful climate outcomes cannot be realized without a foundation of quality economic growth, structural transformation, and reductions in energy and carbon intensity. These crucial conditions can make climate policy more sustainable and effective over the long term.

5. Conclusions

To summarize, this study examines the relationships between macroeconomic performance and the achievability of stated targets in the GHG, RE, and EE pillars of the Paris Agreement from 1990 to 2023. It examines the relationship between economic success and environmental impact with the LMDI decomposition methodology based on the Kaya identity in addition.

The results of the analysis of the global climate policy show high heterogeneity in the data, and questions arise primarily about the origins of the data, starting with the formation of climate policy and ending by the lack of properly structured dataset of different climate-related targets. In general, the results obtained do not fully confirm the original hypothesis that there is a pronounced influence of economic factors on the degree of achievability of the stated targets according to the constructed Bayesian Tobit regression models. Hence, the main problem, which covers not only this study, but the entire field of economic and environmental performance analysis.

This reflects a broader issue that extends beyond the scope of this study: weak methodology and the lack of responsibility for setting up and achieving targets. One of the key observations is that many formally overachieved targets were set up without taking into account the dynamics of national economic system. This outcome suggests either the low ambition of those targets or a disconnection between target-setting and actual emission trajectories or the lack of connection with reality. Conversely, countries without any formally stated targets also experienced steady emissions growth, albeit at a less pronounced rate than their overachieving counterparts. Thus, the mere existence or absence of a target does not determine progress. Substantial emissions reductions were observed only in countries that adopted moderate, realistic, scientifically and institutionally grounded targets. These findings underscore that targets should not be “impressive” in appearance, but feasible and tailored to the economic and governance realities of each country.

Furthermore, the comparison of stated climate goals with LMDI decomposition highlights the contradictions between economic growth and emissions trajectories. In countries where growth was accompanied by reductions in energy and carbon intensity, emissions declined meaningfully. However, in many other cases, rising GDP and energy consumption conflicted with declared climate ambitions. This points to the risk of “blind” climate regulation, in which environmental objectives are pursued without adequate consideration of economic constraints, potentially leading to economic decline, which in turn may result in long-term environmental degradation. This creates a self-reinforcing cycle that can only be broken by a genuine balance between ecological and economic interests.

An additional factor explaining the paradox of emission reductions in developed countries is the relocation of energy-intensive industries to emerging economies (

Table 7. Share of industrial GVA in GDP by income level.

Income Group	Number of Countries	Total GVA of Industry in 2000, Bln USD_2011	Total GVA of Industry in 2020, Bln USD_2011	Change in GVA, %	Total Energy Consumption of Industry in 2000, Mln TJ	Total Energy Consumption of Industry in 2020, Mln TJ	Change in Energy Consumption, %
High-income industrial economies	33	8137.07	9504.86	16.81	97.31	80.50	−17.27
High-income industrializing economies	10	653.36	1331.01	103.72	5.76	9.67	67.88
Middle-income industrial economies	30	6228.67	16,225.16	160.49	67.23	160.84	139.25
Middle-income industrializing economies	41	1484.33	4136.80	178.70	19.38	34.63	78.73
Low-income economies	9	154.54	237.33	53.58	1.14	1.88	65.42

). According to WDI data [116], between 2000 and 2020, the total gross value added (GVA) of the industrial sector in industrializing middle-income countries nearly tripled, while in high-income countries it increased by just 17%, accompanied by a decline in industrial energy consumption. This suggests that the climate “success” of developed countries may be partly attributed to the outsourcing of emissions-intensive production, raising critical questions about the equity and transparency of global climate mitigation responsibilities.

To date, there is no single global emissions accounting and control system that allocates responsibility for emissions between producers and consumers [117,118], there is no common framework for investment flows and costs in the green economy [119], and there is no explicit transparency of climate regulation [120]. This also reveals the risk that global climate initiatives use disclosure for “greening” purpose [121,122] to make misleading claims about their climate commitments and results, rather than reporting meaningful climate outcomes.

According to [123], greater transparency about goals, activities, and achievements is necessary, not only to meet the needs of the public, but also for climate initiatives themselves. It is crucial for national and international policymakers to be able to track the progress of sub-state and non-state actors [124] in order to better assess national and global progress in combating climate change [125]. This will allow us to understand which measures are most effective and which are misguiding [120]. For global climate initiatives to achieve their stated effects and push countries towards more ambitious goals, they must be transparent about their results, progress, and operations. The information gaps identified in this analysis—both quantitative and qualitative—must be addressed in order to strengthen trust in the global climate agenda, enhance policy effectiveness, and ensure real progress on the path toward a balanced and sustainable environmental future.

One of the key directions for future research lies in the development of methodologies for setting scientifically grounded, realistic, and institutionally supported climate targets. As demonstrated by the analysis, overly ambitious or purely declarative objectives– formulated without consideration of socio-economic and institutional constraints—not only fail to drive emissions reductions but may also erode trust in the broader climate agenda. The conceptual diagram of systemic barriers (Figure 4) clearly illustrates that climate policymaking is filtered through numerous layers, including scientific uncertainty, interpretative distortions, and political conflicts of interest. This underscores the need to establish an interdisciplinary foundation and implement transparent verification mechanisms, both at the stage of goal-setting and during policy execution. Future research should focus on integrating scientific evidence, scenario-based modeling, and institutional capacity within a unified framework for designing climate strategies that align environmental ambitions with real development potential.