Carbon Emissions and National Sustainable Development Goals Coupling Coordination Degree Study from a Global Perspective: Characteristics, Heterogeneity, and Spatial Effects

Abstract

While the environmental issues resulting from carbon emissions are widely recognized by the public, there is a lack of in-depth research on whether controlling carbon emissions will have an impact on social development at the national level. In this study, we used panel data from 166 countries worldwide from 2005 to 2020 to construct coupled coordination models, panel regression models, and spatial Durbin models to assess the impact of carbon emissions on the coupled coordinated development of sustainable development goals (SDGs) in different countries around the world. This study is the first to investigate the features, heterogeneity, and spatial effects of carbon emissions on the SDG coupling coordination degree. We found that: (1) Globally, the SDG coupling coordination degree was generally low from 2005 to 2020. Despite the slow improvement in the global SDG coupling coordination degree over the past 16 years, it has always been in a barely imbalanced transitional development state. At the same time, there was a phenomenon of “high-income > high–middle-income > low–middle-income > low-income” difference in the SDG coupling coordination development level among countries of different income types. (2) Carbon emissions have a significant promoting effect on the SDG coupling coordination degree for low-income and low–middle-income countries while having a significant inhibitory effect on the SDG coupling coordination degree for high–middle-income and high-income countries. In addition, the impact of carbon emissions on the SDG coupling coordination degree also exhibits regional heterogeneity. (3) Carbon emissions have a significant spatial spillover effect on the SDG coupling coordination degree, with a positive promoting effect on the SDG coupling coordination degree in one’s own country and a significant inhibitory effect on the SDG coupling coordination degrees of neighboring countries. Our research can help decision-makers develop targeted carbon reduction plans to avoid social development losses caused by controlling carbon emissions in a crude manner. Our research findings provide crucial evidence for the formulation of international policies aimed at reducing carbon emissions, in order to avoid the inequities that may result from the adoption of extreme carbon reduction policies.

1. Introduction

As the global population continues to grow and the economy rapidly develops, global carbon dioxide emissions are increasing, leading to increasingly serious problems such as climate change and sea level rise [1]. According to data released by the International Energy Agency (IEA) (Data source: https://www.iea.org/reports/global-energy-review-2021/co2-emissions?language=zh (accessed on 20 May 2023)), global carbon dioxide emissions reached 34 billion tons in 2020, with China, the United States, and India being the three countries with the highest emissions in the world. Meanwhile, the European Union is also one of the world’s largest carbon-emitting regions. To address the issue of climate change, many countries and regions around the world have taken a series of emission reduction actions, such as developing new energy sources, improving energy efficiency, and promoting low-carbon lifestyles [2]. These measures have slowed the growth rate of CO₂ emissions, but emissions still show an increasing trend, which is detrimental to sustainable development. Therefore, more powerful actions are needed to accelerate the transition to clean energy and put the world on the path to achieving climate and sustainable development goals (SDGs) [3]. At the same time, facing global challenges such as poverty, inequality, climate change, environmental degradation, and declining prosperity, the United Nations’ SDGs have become an important global issue of concern (Data source: https://www.iea.org/reports/co2-emissions-in-2022 (accessed on 20 May 2023)). The SDGs are a global and long-term development framework and are of great significance for achieving the sustainable development of mankind and the earth. Reducing carbon emissions is theoretically an important measure for achieving SDGs [4]. However, in practice, carbon emissions may have different impacts on SDGs or their sub-goals [5]. Despite this, the characteristics, heterogeneity, and spatial effects of carbon emissions on the coupling coordination of the entire system of SDGs remain unknown. Therefore, exploring the coupling coordination situation of the global SDGs and the impact of carbon emissions on the SDG coupling coordination is a complex and critical issue that requires further exploration.

2. Literature Review

In theory, reducing carbon emissions is an important measure for achieving sustainable development goals (SDGs) [4,6]. The SDGs are a global framework developed by the United Nations aimed at eliminating poverty, promoting social justice, protecting the environment, and driving economic development [7]. It includes 17 goals and 169 specific indicators covering various domains. Among them, SDG 13 “Take urgent action to combat climate change and its impacts” aims to reduce global greenhouse gas emissions, enhance climate resilience and adaptive capacity, and strengthen coordination and cooperation on climate-related issues [8]. Carbon emissions are one of the main causes of climate change; therefore, achieving Goal 13 requires reducing carbon emissions. Furthermore, reducing carbon emissions is necessary to achieve other SDGs, such as SDG 7 “Ensure access to affordable, reliable, sustainable, and modern energy”, which can be achieved through the development of clean energy and improving energy efficiency to reduce carbon emissions [9]. Thus, reducing carbon emissions is an important measure for achieving SDGs.

Empirical research suggests that carbon emissions can have diverse impacts on SDGs or their sub-goals. On the one hand, reducing carbon emissions can promote the harmonious development of the economy, society, and environment through measures such as mitigating climate change [10], improving air quality [11], enhancing energy security [12], creating employment [13], and promoting sustainable development. This supports the global achievement of SDGs. On the other hand, for some countries, reducing carbon emissions may have an adverse impact on SDGs. Specifically, these studies can be summarized into the impact of carbon emissions on economic, social, and environmental aspects of sustainable development.

2.1. The Impact of Carbon Emissions on Economic Development

Carbon emissions resulting from global climate change pose significant challenges to sustainable economic development on a global scale [14]. There is a complex and interdependent relationship between carbon emissions and economic development. Specifically, for countries rich in petroleum resources, the impact of carbon emissions on economic growth is generally negative, as increased CO₂ emissions often lead to inhibited economic growth [15,16]. However, at the global level, an increase in carbon emissions can actually stimulate economic growth in the short term [17]. Moreover, there exists a bidirectional causal relationship between carbon emissions and economic growth [18,19]. This relationship is observed across different countries [20,21], levels of economic development [19], industries [22], and periods [23,24]. In the short term, there is a bidirectional causal relationship between economic growth and carbon emissions among 12 Asia-Pacific economies [23], and significant long-term and short-term causal relationships between CO₂ emissions and economic growth in Pakistan [24]. Additionally, there exists a bidirectional causal relationship between agricultural carbon emissions and agricultural economic growth in various provinces of China in both the short and long term [22]. For low-income countries, reducing carbon emissions may constrain the economic development of certain regions and industries, thereby exacerbating poverty [25]. Pursuing economic growth at the expense of natural environments is an immoral form of development. Energy consumption is the primary cause of CO₂ emissions, which exacerbates global warming [21]. The decoupling of carbon emissions and economic growth primarily depends on the decoupling of economic activity and energy consumption [26]. Furthermore, the nonlinear relationship between economic growth and CO₂ emissions follows an environmental Kuznets curve and exhibits significant regional differences [27,28,29].

2.2. The Impact of Carbon Emissions on Social Development

Carbon emissions have posed serious challenges to global sustainable development by causing climate change on a worldwide scale [30]. The impact of carbon emissions can be seen in all aspects of social development, including population growth, poverty, employment, pricing, health, food security, and energy security. In the areas of employment and poverty, the transition to a low-carbon economy and climate change regulations will impact employment opportunities and further affect social poverty [31,32]. For instance, transitioning to a low-carbon economy can create new job opportunities in clean energy and other sectors, leading to economic growth and poverty reduction [32,33]. However, since the development of clean and renewable energy technologies requires workers to possess higher skills and qualifications, the development of these industries may lead to a reduction in work opportunities in traditional energy sectors, potentially resulting in social instability [34]. Regarding pricing and poverty, research conducted across 98 developed and developing countries in 2011 found that there is a positive correlation between carbon emissions and a “reduction in welfare” effect on the level of pricing, as well as a positive correlation between poverty rates and carbon emissions, in line with the “pollution haven” hypothesis [35]. In terms of health, reducing carbon emissions can improve air quality, enhance public health, and reduce the burden on healthcare systems [36,37]. Concerning food security, global warming affects agricultural production in various ways such as rainfall, temperature, and carbon emissions, seriously threatening food security [38]. In the area of energy security, actively promoting renewable energy is an essential measure for reducing carbon emissions. By promoting the use of renewable energy and improving energy efficiency, energy dependence on foreign oil can be reduced, and the elasticity of the energy system can be increased to enhance energy security [39,40,41]. Additionally, reducing carbon emissions is often accompanied by a reduction in fossil fuel usage, which can lead to problems affecting economic growth, such as energy supply shortages and industrial restructuring [42,43]. Low-carbon development is one of the key mechanisms for achieving green economic growth, social welfare and development, and environmental protection and management [44].

2.3. The Impact of Carbon Emissions on the Ecological Environment

The increasing levels of carbon emissions have resulted in global climate change, posing significant threats to environmental sustainability and garnering attention from the international community [45]. Governments across the world have implemented various laws and regulations to encourage corporations to actively reduce their carbon footprint, including the implementation of carbon taxes, which play an essential and positive role in environmental protection [46]. Typically, as more countries implement carbon taxes to improve environmental quality, and the public perceives the benefits of carbon taxation for the environment, corporations invest in reducing their carbon emissions to lower their carbon tax costs [18,47]. However, when the carbon tax is set at a moderate level or when there are high fluctuations in carbon taxation and demand, some free-riding behavior may occur, resulting in carbon taxation not always being beneficial to the environment [48]. The use of fossil fuels and GDP both contribute to short-term and long-term increases in carbon emissions. However, the use of renewable energy sources is vital to achieving environmental sustainability goals. The burning of fossil fuels is the primary source of CO₂ emissions, although household carbon emissions and land use changes are also recognized as significant contributors [49,50,51]. Furthermore, there is a significant correlation between tourism, environmental quality, and carbon emissions. As such, research on environmental sustainability and carbon emissions often centers around the tourism industry [52]. Therefore, it is crucial for governments to focus on ecological tourism policies and energy resources to reduce carbon emissions and promote sustainable development. Carbon emissions are the primary factor in environmental degradation and are related to climate change and global warming, placing environmental sustainability at risk [53]. Reducing the ecological footprint, CO₂ and greenhouse gas emissions, and exposure to air pollution through promoting innovation, improving trade policies, fostering political and legal institutions, and promoting renewable energy are effective ways to address environmental sustainability [54]. This can ultimately enhance human development and enable better environmental sustainability [55].

However, current literature on the impact of carbon emissions on SDGs has only focused on individual sub-goals or a few sub-goals, failing to address the entire system of SDGs and neglecting the coupling coordination development within the system. Additionally, these studies have overlooked the influence of heterogeneous characteristics among different countries. The conclusions drawn from these studies exhibit inconsistencies, which may be attributed to the diverse policies, institutions, and economic development levels of multiple country samples [15]. Therefore, considering differences in geographical location, economic development levels, and resource endowments [33], it is unclear whether carbon emissions on the SDG coupling coordination degree varies significantly across regions and economic development levels among different countries. Furthermore, since carbon emissions typically have spillover effects [52], we are also interested in examining whether there are spatial effects on carbon emissions on the SDG coupling coordination. In summary, current research has not addressed the characteristics, heterogeneity, and spatial effects of carbon emissions on the SDG coupling coordination with the entire system, leaving a gap in the literature. Only a few studies have indicated that carbon emissions have significant effects on individual sub-goals or a few sub-goals of sustainable development goals, but this provides some theoretical support for further research aimed at filling the aforementioned gaps.

2.4. Research Questions and Innovation

The key research questions we aim to address are: First, what are the characteristics of the coupling coordination degree of global SDGs? Second, what is the impact of carbon emissions on the coupling coordination degree of national SDGs in different geographical regions, countries with different income levels, and from different spatial perspectives? Third, how can carbon emission reduction policies be formulated in a way that aligns with a country’s current development status? We will examine the impact characteristics of the coupling coordination degree between carbon emissions and sustainable development goals through the complexities of the effect of carbon emissions on individual sub-goals, differences in development among countries, and different spatial perspectives. These research questions are crucial for promoting global cooperation, mitigating climate change, and ensuring the sustainable development of human society. Additionally, this research can facilitate the formulation of relevant policies and measures for countries at varying levels of development, promote the coordinated development between carbon emission reduction and SDGs, and avoid unnecessary negative impacts. Furthermore, the research can optimize resource allocation and industrial structures, and promote the synergistic development of the economy, society, and environment, ultimately achieving sustainable development.

To address these issues, we first utilized panel data from 166 countries between 2005 and 2020 and used a coupled coordination model to calculate the coupling coordination degree of SDGs for each country. Next, we considered regional and income-level heterogeneity and established a panel regression model to study the impact of carbon emissions on the coupling coordination degree of SDGs for different countries. Finally, we included a spatial weight matrix in a general panel regression model, expanding it into a spatial Durbin model, to study the spatial effects of carbon emissions on the coupling coordination degree of SDGs. Therefore, the main contributions of this paper are as follows:

(1)

We systematically evaluated the coupling coordination degree of SDGs for 166 countries using 90 indicators, and analyzed the overall coupling coordination development characteristics of each country’s SDGs.

(2)

Unlike previous studies that only examined the impact of carbon emissions on a single or a few sub-goals of SDGs, and considered geographical location and development differences among countries, this study comprehensively investigates the heterogeneous impact of carbon emissions on the coupling coordination degree of SDGs by region and income type.

(3)

In order to further explore the spatial effects of carbon emissions, we constructed a spatial Durbin model and established an economic geography spatial weight matrix, evaluating for the first time the spatial spillover effects of carbon emissions on the coupling coordination degree of SDGs.

(4)

Our research is helpful for decision-makers to formulate targeted carbon emission reduction plans, and strengthen cooperation among nations, especially those geographically close to each other, so as to better control carbon emissions and achieve coordinated development of SDGs in the future.

The structure of the remaining sections of this paper is as follows: Section 3 introduces the research methods, variable models, and data sources; Section 4 presents the research results on the impact of carbon emissions on the coupling coordination degree of SDGs in different countries; Section 5 discusses the findings; and the final section includes the main conclusions.

3. Methods, Models, and Data Sources

3.1. Entropy Method

We used the entropy method to measure the comprehensive index of each sub-goal of SDG1–SDG17. The entropy method calculates the weight of each indicator based on the information content of the indicator data in the sample, which effectively avoids the subjectivity of indicator selection and weighting [56]. Compared with subjective weighting methods, it has higher credibility [57]. The specific steps are as follows:

(1)

Arrange the n evaluation indicators of m samples in order and form a raw data matrix, $X={(x_{ij})}_{m\times n}(1\le i\le m,1\le j\le n)$, where the i indicator of the j sample is represented as $X_{ij}$.

(2)

Normalize the data to make them comparable. We choose the extreme value method, which has the best processing effect, and let:

$$k_{ij}=\max (x_{ij})-x_{ij}/\max (x_{ij})-\min (x_{ij})(i=1,2,\cdots ,m;j=1,2,\cdots ,n)$$

(3)

Calculate the weight matrix of the indicator system: $p_{ij}=k_{ij}/{\displaystyle \sum _{i=1}^{m}k{i}_j}(j=1,2,\cdots ,n)$, where $0\le p_{ij}\le 1$.

(4)

Calculate the entropy value of each indicator: $e_j=-h{\displaystyle \sum _{i=1}^m(p_{ij}\times Ln{p}_{ij})},i=1,2,\cdots ,m$, where $h=\frac{1}{Lnm},0\le e_j\le 1$.

(5)

Calculate the coefficient of variation of each indicator: $g_j=1-e_j(j=1,2,\cdots ,n)$.

(6)

Calculate the weight of each indicator: $w_j=g_j/{\displaystyle \sum _{j=1}^n{g}_j}(j=1,2,\cdots ,n),$ where ${\displaystyle \sum _{j=1}^n{w}_j=1}$.

(7)

Calculate the comprehensive score of each sample: $S_i={\displaystyle \sum _{j=1}^n{w}_j}\times k_{ij},$ where $0\le Si\le 1$.

3.2. Coupling Coordination Degree

Coupling is a concept derived from physics, which refers to the phenomenon where two or more systems interact and influence each other in a coordinated manner. The coupling degree describes the extent to which the interaction between systems or elements affects each one. The coordination degree refers to the degree of coordination between two or more systems or elements. The coupling coordination degree reflects the quality of the system’s coordination on the basis of coupling and mutual reliance and is an indicator of the degree to which the system’s coordination is achieved. Therefore, coupling degree mainly reflects the degree of interaction and influence between systems or elements, without distinguishing advantages or disadvantages. Coupling coordination degree, on the other hand, represents the level of benign coupling between systems or elements in their interactions, reflecting the degree of coordination between them.

The normalized formula for the coupling degree model of multiple subsystems is:

$$C={[{\displaystyle \prod _{i=1}^n{U}_i}/{(\frac{1}{n}{\displaystyle \sum _{i=1}^n{U}_i})}^n]}^{\frac{1}{n}}$$

(1)

In Equation (1), n represents the number of subsystems; $U_i$ represents the values of each subsystem, with a distribution range of [0, 1], so the coupling C value ranges from 0 to 1. The larger the C value, the smaller the degree of dispersion between subsystems and the higher the coupling; conversely, the smaller the C value, the lower the coupling between subsystems.

The coordinated development degree model based on Equation (1) will be simplified. On the one hand, because the distribution of C values is concentrated at one end, the calculated D values mainly depend on T values; that is, the development level of the system itself, and the role of the system’s coordination level are weakened, resulting in the difficulty of fully realizing the value and significance of the coupling coordination measurement of the D value. On the other hand, when calculating the coordinated development degree, if the subsystems are defined as equally important, there will be a problem of simplifying the coordinated development degree formula. T should be calculated using arithmetic weighting rather than geometric weighting because using geometric weighting will reduce the range of T values, and the comprehensive evaluation index should reflect the complementary relationship between the subsystems. Therefore, the comprehensive evaluation index T is:

$$T={\displaystyle \sum _{i=1}^n{\alpha }_i\times U_i,{\displaystyle \sum _{i=1}^n{\alpha }_i=1}}$$

(2)

In the formula, $U_i$ is the standardized value of the i-th subsystem and ${\alpha }_i$ is the weight of the i-th subsystem.

Therefore, the calculation formula of coupling coordination degree D is:

$$D=\sqrt{C\times T}=\sqrt{{[{\displaystyle \prod _{i=1}^n{U}_i}/{(\frac{1}{n}{\displaystyle \sum _{i=1}^n{U}_i})}^n]}^{\frac{1}{n}}\times {\displaystyle \sum _{i=1}^n{\alpha }_i{U}_i}}$$

(3)

According to the different sizes of D values, the classification of D is shown in

Table 1. Classification index for coupling coordination degree.

Coordination Stage	CCD	Stage Segmentation	Coordinating Conjunctions
Low-level stage	0~0.3	0~0.2	Severe ataxia
		0.2~0.3	Moderate dysregulation
Antagonistic stage	0.3~0.5	0.3~0.4	Mild discomfort
		0.4~0.5	Near-miss disequilibrium
Break-in Period	0.5~0.8	0.5~0.6	Barely coordinated
		0.6~0.7	Junior coordination
		0.7~0.8	Intermediate coordination
High-level stage	0.8~1	0.8~1	High degree of coordination

.

3.3. Baseline Model

We have quantitatively studied the specific impacts of carbon emissions on the coupling coordination degree of global national SDGs (sustainable development goals) by constructing a panel data measurement model. As we evaluated the level of coordinated development of 17 SDGs through 90 sub-indicators, selecting appropriate control variables was a difficulty that we faced in the model construction process. Control variables refer to variables that affect experimental results other than experimental factors (independent variables), and these variables should be as unrelated as possible to the 90 sub-indicators. We referred to existing research literature related to sustainable development and selected the following control variables: per capita GDP, total population, technological progress, international trade, total capital formation, economic freedom, and political stability [58,59,60,61,62,63,64].

The final constructed panel data model is presented as follows:

$$LnCC{D}_{it}={\alpha }_0+{\alpha }_{1}LnC{E}_{it}+{\alpha }_{n}Ln{X}_{it}+{\mathsf{\xi }}_{it}$$

(4)

In the equation, i represents the country, t represents time, $LnCC{D}_{it}$ denotes the logarithmic value of the coupling coordination degree of national sustainable development goals, and $LnC{E}_{it}$ represents the logarithmic value of carbon emissions. $Ln{X}_{it}$ represents the logarithmic value of selected control variables, including per capita GDP, total population, technological progress, international trade, total capital formation, economic freedom, and political stability. Taking logarithms has two purposes: first, to reduce the difference in scale between variables and prevent heteroscedasticity, and second, to clarify the elasticity coefficient of carbon emissions with respect to the SDG coupling coordination degree. ${\theta }_0,{\theta }_1,$ and ${\theta }_n$ are the corresponding regression estimation coefficients for each variable. We have fixed the time and individual effects in our model. $\mathsf{\xi }$ represents the residual term.

3.3.1. Spatial Durbin Model

This article constructs a spatial panel Durbin model of the impact of carbon emissions on the coupling coordination degree of sustainable development goals, as shown in Equation (5).

$$LnCC{D}_{it}=\rho W_{ij}LnCC{D}_{it}+{\beta }_{1}LnC{E}_{it}+\gamma Ln{X}_{it}+W_{ij}\ast ({\beta }_{3}LnC{E}_{it}+\epsilon Ln{X}_{it})+{\mu }_i$$

(5)

In the equation, $X_{ij}$ represents the control variable, $u_i$ denotes spatial fixed effects, and $W_{ij}$ is the weight matrix; $i$ refers to different countries, and $t$ refers to different time points.

A spatial econometric model requires the design of a spatial weight matrix that reflects the spatial influence of neighboring regions on the focal region. To demonstrate the robustness of the spatial estimation results, this article considers constructing an economic–geographic distance spatial weight matrix from both an economic and geographical perspective. The economic–geographic distance weight matrix $W_{ij}$ constructed in this article is shown in Equation (6):

$$W_{ij}=W_{d}diag(\overline{Y_1}/\overline{ Y },\overline{Y_2}/\overline{ Y },\overline{Y_n}/\overline{ Y })$$

$$W_d=\left\{\begin{array}{c}\frac{1}{d_{ij}} d_{ij}\ge d\\ 0 d_{ij}<d\end{array}\right.$$

(6)

${𝑊}_d$ is a first-order inverse distance geodesic distance weight matrix constructed using the reciprocal of the straight-line Euclidean distance ($d_{ij}$) between the centroids of each country. $\overline{Y_i}={\displaystyle \sum _{t_0}^{t_n}{Y}_{it}}/t_n-t_0+1$ represents the regional per capita GDP of each country i during the observation period, and $\overline{Y}$ represents the per capita GDP of all regions observed.

Previous studies have suggested that using point estimates to test for the existence of spatial spillover effects in the regression coefficients of spatial panel models may lead to bias, and it is necessary to decompose the parameter vector in the spatial panel model into direct and indirect effects using the partial differentiation method [59]. The vector form of the spatial Durbin model can be expressed as:

$$Y_t={(I_n-\rho W)}^{-1}\left(X_t\beta +W{X}_t\theta \right)+{(I_n-\rho W)}^{-1}{\epsilon }_t^{*}$$

(7)

The error term comprises random error, spatial effects, and time effects. The partial differentiation matrix of the explanatory variable relative to the k-th explanatory variable ($x_{ik}$, $i=1, 2\dots , N$) in different spatial units at any given time is:

$${\left. [\frac{\partial Y}{\partial X_{1k}},\frac{\partial Y}{\partial X_{2k}}\dots \frac{\partial Y}{\partial X_{Nk}}\right]}_t=\left. [\begin{array}{l}\frac{\partial y_1}{\partial X_{1k}}\stackrel{}{}\frac{\partial y_1}{\partial X_{2k}}\dots \frac{\partial y_1}{\partial X_{Nk}}\\ \frac{\partial y_2}{\partial X_{1k}}\stackrel{}{}\frac{\partial y_2}{\partial X_{2k}}\dots \frac{\partial y_2}{\partial X_{Nk}}\\ \underset{}{}\dots \underset{}{}\underset{}{}\underset{}{}\underset{}{} \dots \underset{}{}\underset{}{}\underset{}{}\underset{}{} \dots \\ \frac{\partial y_N}{\partial X_{1k}}\stackrel{}{}\frac{\partial y_N}{\partial X_{2k}}\dots \frac{\partial y_N}{\partial X_{Nk}}\end{array}\right]=(I_n-\rho W)\left. [\begin{array}{l}\stackrel{}{}{\beta }_k\stackrel{}{}\stackrel{}{}{w}_{12}{\theta }_k\dots w_{1N}{\theta }_k\\ w_{21}{\theta }_k\stackrel{}{}\stackrel{}{}{\beta }_k\stackrel{}{}\dots w_{2N}{\theta }_k\\ \underset{}{}\dots \underset{}{}\underset{}{}\underset{}{}\underset{}{} \dots \underset{}{}\underset{}{}\underset{}{}\underset{}{} \dots \\ w_{N1}{\theta }_k\stackrel{}{}{w}_{N2}{\beta }_k\dots {\beta }_k\end{array}\right]$$

(8)

In the equation above, the direct effect is the mean of the diagonal elements in the partial differentiation matrix on the right-hand side, while the indirect effect is the mean of the corresponding row or column of non-diagonal elements in this matrix.

Before conducting spatial econometric analyses, it is necessary to first examine the existence of spatial effects for both carbon emissions and the SDG coupling coordination degree. Moran’s I index was used to test the spatial autocorrelation of both variables, and the results are shown in

Table 2. Spatial correlation test results from 2005 to 2020.

	Moran’s I Value
	LnCE	LnCCD
2005	0.269 ***	0.511 ***
2006	0.267 ***	0.512 ***
2007	0.262 ***	0.526 ***
2008	0.256 ***	0.526 ***
2009	0.251 ***	0.521 ***
2010	0.245 ***	0.524 ***
2011	0.236 ***	0.313 ***
2012	0.229 ***	0.520 ***
2013	0.228 ***	0.520 ***
2014	0.220 ***	0.519 ***
2015	0.220 ***	0.512 ***
2016	0.215 ***	0.510 ***
2017	0.212 ***	0.506 ***
2018	0.209 ***	0.502 ***
2019	0.204 ***	0.498 ***
2020	0.199 ***	0.494 ***

Note: The asterisks indicate the level of statistical significance of a test, with p-values less than 0.01 denoted by three asterisks (***).

. The test results indicate that the Moran’s I indices of carbon emissions and the SDG coupling coordination degree under the economic–geographic distance weight matrix during the period 2005–2020 were significantly positive at the 1% level, indicating that there was significant spatial autocorrelation for both variables among all countries during the examination period and that they exhibited space clustering distribution characteristics. Therefore, using spatial econometric analysis is reasonable and feasible. The appropriate spatial econometric model should be selected for empirical analysis. The article then conducted LM tests, Hausman tests, LR tests, and Wald tests sequentially. The results of the above tests all show that the spatial Durbin model with fixed regional effects is the optimal choice.

3.3.2. Variable

Coupling Coordination Degree of Sustainable Development Goals (CCD). The evaluation system of Sustainable Development Goals (SDGs) published by the United Nations contains 17 sub-goals. We first use the entropy method to synthesize the evaluation indicators of each sub-goal into a comprehensive index, and then calculate the CCD of SDGs based on the comprehensive indexes obtained for SDG1–SDG17. Considering the availability and practicality of data, we have selected the indicators shown in

Table 3. Evaluation index of CCD for SDGs.

Sub-Goals	Definition and Explanation
SDG1	1. Poverty headcount ratio at USD 1.90/day (%). 2. Poverty headcount ratio at USD 2.15/day (%).
SDG2	3. Sustainable Nitrogen Management Index. 4. Prevalence of obesity, BMI ≥ 30. 5. Human trophic level. 6. Mortality rate, children under 5 years of age. 7. Prevalence of wasting in children under 5 years of age. 8. Permanent cropland. 9. Farmland areas per person. 10. Fertilizer consumption. 11. Cereal yield. 12. Incidence of malnutrition.
SDG3	13. Maternal mortality rate. 14. Neonatal mortality rate. 15. Pulmonary tuberculosis morbidity rate. 16. Subjective well-being. 17. Life expectancy, female. 18. Life expectancy, male. 19. Births attended by skilled health personnel. 20. New HIV infections. 21. Traffic deaths. 22. Adolescent fertility rate. 23. Surviving infants who received 2 WHO-recommended vaccines.
SDG4	24. Net primary enrollment rate. 25. Educational outlay. 26. Literacy rate. 27. Secondary school enrollment rate. 28. Tertiary educational attainment.
SDG5	29. Ratio of female-to-male labor force participation rate. 30. Number of female employed. 31. Primary school female-to-male ratio. 32. Middle school female-to-male ratio. 33. Number of female unemployed. 34. Demands of family planning met by modern methods. 35. Ratio of male-to-female years of education. 36. Proportion of women in Congress. 37. Ratio of male-to-female labor force participation.
SDG6	38. Population using at least basic drinking water services. 39. Population using at least basic sanitation services. 40. Freshwater withdrawal.
SDG7	41. Population with access to electricity. 42. Population with access to clean fuels and technology for cooking. 43. Share of renewable energy in total primary energy supply. 44. Electric plating power consumption. 45. The time required for electrification.
SDG8	46. Adjusted GDP growth. 47. Unemployment rate. 48. Inflation rate. 49. Total number of unemployed. 50. Total savings. 51. Total industrial output value. 52. GNI. 53. Total capital formation. 54. GDP per capita.
SDG9	55. Population using the internet. 56. Mobile broadband subscriptions. 57. Scientific and technical journal articles. 58. Expenditure on research and development. 59. Researchers. 60. Gap in internet access by income.
SDG10	61. Gini coefficient adjusted for top income. 62. Palma ratio.
SDG11	63. Access to improved water sources, piped. 64. Population density. 65. Urbanization rate. 66. Road traffic injury. 67. PM2.5.
SDG12	68. Production-based SO2 emissions. 69. Production-based nitrogen emissions. 70. Non-recycled municipal solid waste.
SDG13	71. CO2 emissions from fossil fuel combustion and cement production. 72. Energy intensity level of primary energy. 73. CO2 emissions embodied in imports. 74. CO2 emissions embodied in fossil fuel exports.
SDG14	75. Fish caught from overexploited or collapsed stocks. 76. Ocean Health Index: Clean Waters score. 77. Mean area that is protected in marine sites important to biodiversity.
SDG15	78. Marine biodiversity threats embodied in imports. 79. Mean area that is protected in terrestrial sites important to biodiversity. 80. Mean area that is protected in freshwater sites important to biodiversity. 81. Red List Index of species survival. Permanent deforestation. 82. Terrestrial and freshwater biodiversity threats embodied in imports. 83. Proportion of forest area. Forest area. Number of threatened mammals. 84. Forest area, hectares.
SDG16	85. Homicides (per 100,000 population). 86. Corruption Perception Index.
SDG17	87. Government spending on health and education. 88. Extracted profits of multinationals. 89. Other countries: government revenue excluding grants (% of GDP). 90. Net official development assistance (ODA) received.

.

Carbon Emissions (CE) are typically characterized by per capita carbon emissions, carbon emission intensity, and total carbon emissions. All three proxy indicators are capable of reflecting the level of carbon emission activity in a country or region. In this study, we have chosen to focus on a country’s total carbon emissions. Since the controlling variables already include total population and economic development indicators, per capita carbon emissions or carbon emission intensity were not used, to avoid potential collinearity issues.

Gross Domestic Product per capita (pGDP) is an effective tool for reflecting the macroeconomic performance of a country or region, measured by the per capita output of goods and services. pGDP is positively correlated with economic development and is therefore commonly used to measure the level of economic growth. Economic growth is one of the key factors affecting sustainable development, providing an important material basis for achieving SDGs [57].

Population (POP) is a term used to represent the number of people in a country or region. The size, structure, growth rate, and migration patterns of a population have a significant impact on the sustainable development of a country or region’s social, economic, resource, and environmental systems [58]. For example, population growth can provide sustained growth momentum for the internal needs of an entire society, significantly enhancing a country’s strength. However, excessive population growth can also create burdens and pressures on society, resources, and the environment, posing severe challenges to sustainable development [65].

Technological progress (TECH) is characterized by the number of scientific and technical publications. It is a major means of improving energy efficiency, developing renewable energy, and creating social productivity, which greatly promotes sustainable development in countries or regions [60].

International Trade (IT) refers to the import and export values of goods and services. IT enables the rational combination and allocation of resources on a global scale, promoting the optimized allocation of resources and the sustainable development of trade and the environment [59]. However, international trade promotes the expansion of economic scale, which undoubtedly increases resource consumption and exacerbates resource depletion and environmental degradation. In addition, international trade can also lead to the adverse consequences of pollution transfer and diffusion worldwide, which is not conducive to sustainable development [66].

Gross Capital Formation (GCF) is an economic indicator that reflects the domestic demand and the impact of investment on the economy of a country or region. Both developed and developing countries require significant capital investments in order to achieve sustainable development goals, and therefore GCF plays a crucial role in sustainable development [61].

Economic Freedom Index (EFD) refers to the degree to which a government refrains from intervening or protects the freedom of competition, the free market, free choice, free trade, and private property within the constitutional framework. It is also an important indicator for evaluating the degree of marketization. A higher score on an index indicates a higher level of government intervention in the economy, resulting in lower economic freedom. The average value obtained by adding up various indicators can be used to calculate the overall coefficient. Countries or regions with more economic freedom tend to have higher long-term economic growth rates and greater prosperity compared to those with less economic freedom. Economic freedom, through the free flow of products, resources, technology, and services between countries and regions, helps to optimize resource allocation, create more opportunities, improve living standards, and affect sustainable development [62].

Political stability (PS) refers to the dynamic orderliness and continuity of a certain society’s political system. It is the basis and condition for sustainable development, as it enables the society to coordinate its economy, population, resources, and environment to form a virtuous cycle system [63] (see

Table 4. Descriptive statistics for the variables.

Variable	Obs	Mean	Std. dev.	Min	Max
LnCCD	2656	−0.585	0.161	−1.211	−0.297
LnCE	2656	9.802	2.195	4.248	16.205
LnpGDP	2656	8.502	1.492	5.022	11.725
LnPOP	2656	16.109	1.670	11.993	21.068
LnTECH	2656	7.795	2.682	−7.002	14.321
LnIT	2656	22.536	7.698	−6.908	29.350
LnGCF	2656	3.162	0.351	−0.088	4.374
LnEFD	2656	3.617	0.472	2.079	4.575
LnPS	2656	0.938	0.531	−4.605	1.591

).

3.3.3. Data Sources

This article primarily uses data from the World Bank database. The carbon emissions data were sourced from the Carbon Dioxide Information Analysis Center at Oak Ridge National Laboratory in Tennessee, USA. The economic freedom data were obtained from annual reports published by The Wall Street Journal and The Heritage Foundation. The political stability data are based on calculations from over 30 foundational data sources, which report on governance perceptions assessed by numerous surveyed individuals and expert evaluations worldwide. Further information on the underlying data sources, aggregation methods, and indicators can be found in the WGI Methodology Paper [67]. There are some missing values in the data published by the World Bank database. Rather than using interpolation to fill these gaps, this article follows the approach adopted in the United Nations’ Sustainable Development Goals report by using data from the previous year to replace the missing data for this year.

In addition, regarding the classification of countries with different income levels, this study used the economic grouping standards for countries released by the World Bank in 2020, as shown in

Table 5. 2020 World Bank income grouping standard for economies.

Country Grouping	Classification Standard (Per Capita Gross National Income)
Low-income countries	Below USD 1035
Low–middle-income countries	USD 1036–4045
High–middle-income countries	USD 4046–12,535
High-income countries	Above USD 12,536

Source: World Bank.

. This classification method first converts the per capita GNI of each economy calculated in local currency into per capita GNI calculated in USD and then classifies the 189 World Bank member countries and 28 economies with populations exceeding 30,000 into different income groups based on current income thresholds. Using this method, the World Bank divides the world’s economies into four groups: low-income, low–middle-income, high–middle-income, and high-income. Typically, low–middle-income countries are referred to as developing countries, while high-income countries are referred to as developed countries. As the economic development level of each country will change slightly over time, in this study, we use the income classification of countries in 2020 as the standard.

4. Results

4.1. Temporal Evolution Trend

From a global perspective, the overall SDG (sustainable development goal) coupling coordination degree during 2005–2020 was low (Figure 1). Although it has been gradually improving, the speed has been very slow, and it has been in a barely uncoordinated state of transitional development for the past 16 years. From the perspective of countries with different income levels, there are significant differences in the level of coupling coordination development of SDGs among different income-level countries during the study period, showing a phenomenon of “high income > upper-middle income > lower-middle income > low income”. Specifically, low-income countries have been in a mild uncoordinated state all along; lower-middle-income countries have changed from a mild uncoordinated state to a near uncoordinated state since 2005 and have not surpassed this state since then; upper-middle-income countries were in a barely uncoordinated state during 2005–2013, and then changed to primary coordination; high-income countries have always been in a state of primary coordination. This also reflects that the level of economic development of a country is a key factor in the development of SDG coupling coordination. Countries with a higher economic development level tend to have better coupling coordination development levels of SDGs. In addition, low-income countries have poor development levels of the SDG coupling coordination degree and have been trapped in a mild uncoordinated state. How to solve the “uncoordinated” predicament of low-income countries is a problem worth paying attention to in the future.

4.2. Evolutionary Trends in Spatial Transformations

During the period 2005–2020, with the passage of time, the coupling coordination development level of SDGs (sustainable development goals) in 166 countries worldwide has been significantly improved. The coupling coordination development level of SDGs in each country has gradually increased, and the area of the growth zone has further expanded. However, the overall level of the SDG coupling coordination development in the world is still relatively low, and no country has reached the high-level coupling stage yet. However, it is still developing in a relatively slow trend toward improvement (Figure 2). From the perspective of spatial distribution, except for some African countries that are still in the antagonistic stage, regions such as North America, South America, Europe, Asia, and Oceania are basically in the stage of adaptation and grinding-in. The SDG coupling coordination development in different regions is very uneven and closely related to the economic development of a country or region. The regions with better coupling coordination development levels of SDGs are mainly concentrated in economically developed Europe, while the regions with poor coupling coordination development levels are mostly concentrated in backward and poor Africa, showing a spatial distribution feature of “low in Africa, high in Europe”.

In 2005, the overall coupling coordination development level of SDGs in the world was in the transitional development class, with 49 countries on the verge of imbalance and 66 countries in a state of marginal coordination (Figure 2a). In addition, only 8 countries were in a state of mild imbalance (imbalanced decline class) and 44 countries were in the primary coordination stage (coordinated development class). From the perspective of spatial distribution, countries in the imbalanced decline class are mainly concentrated in Africa, such as Mauritania, Niger, Sudan, and Somalia, as well as Afghanistan and Yemen in Asia. Among the transitional development countries, 71.9% are distributed in Asia and Africa, while the remaining ones are in North America, South America, and Europe, and these regions have basically reached the level of marginal coordination. The majority of coordinated development countries are distributed in Europe, accounting for 70.4%. The remaining ones are Canada, Dominican Republic, and the United States in North America; Australia and New Zealand in Oceania; Gabon in Africa; Argentina, Brazil, and Colombia in South America; and Israel, Japan, Korea, and Singapore in Asia; these countries are all middle-to-high income or high-income countries with a high level of economic development.

Compared to 2005, there was a certain improvement in the global coupling coordination development of SDGs in 2010. The number of countries in a state of mild imbalance decreased to six, still including Afghanistan in Asia and five countries in Africa. The number of countries on the verge of imbalance reduced, the number of countries in a state of marginal coordination increased, and the number of countries in the primary coordination stage increased to 54 (Figure 2b). In addition, the coupling coordination development level of SDGs in countries such as Germany, France, the United Kingdom, and Italy in Europe was upgraded from primary coordination in 2005 to intermediate coordination.

In 2015, there was a significant improvement in the global coupling coordination development of SDGs, continuing the trend of increasing levels of development observed in previous years (Figure 2c). From the perspective of spatial distribution, Africa mainly remained at the lower levels of coupling coordination development, including mild imbalance, on the verge of imbalance, and marginal coordination. North America and South America were mainly at the marginal coordination and primary coordination levels, Oceania was mainly at the primary coordination level, and Europe was mainly at the primary and intermediate coordination levels. By contrast, Asia covered a range from mild imbalance to primary coordination level, which is consistent with the spatial distribution observed in 2005 and 2010. These results suggest that the global coupling coordination development of SDGs exhibits significant spatial agglomeration characteristics and regional heterogeneity.

As of 2020, the coupling coordination development of SDGs globally has transitioned into the transitional development and coordination development categories, with 65 countries being in a state of mild imbalance and 63 countries being in a primary coordination state (Figure 2d). In comparison to 2005, there has been significant growth in the level of SDG coupling coordination development. From a spatial distribution perspective, African countries, except for economically developed nations such as Gabon and Mauritius, are mainly in a state of primary coordination, while the rest are in a state of mild imbalance, on the verge of imbalance, or even in a state of imbalance such as Somalia, South Sudan, and Chad. In North and South America, the SDG coupling coordination development is mainly in a state of mild imbalance and primary coordination, with an incremental increase from mild imbalance to primary coordination. However, the overall SDG coupling coordination development of North American countries is higher than those of South American countries. Oceania’s SDG coupling coordination development is relatively stable, remaining in a state of primary coordination. As for Europe, it is generally in a state of primary coordination and intermediate coordination and is regarded as the region with the best coupling coordination development of SDGs. However, some countries in Europe are still in a state of mild imbalance, but their coupling coordination development of SDGs is gradually improving. The number of countries in a state of mild imbalance has decreased from ten in 2005 to four in 2020. Lastly, in Asia, the coupling coordination development of SDGs varies greatly. Countries such as Azerbaijan, Georgia, and Israel, which are contiguous with Europe, as well as East Asian countries such as Japan and South Korea, and Southeast Asian countries such as Indonesia, Malaysia, Singapore, Thailand, etc., are in a state of primary coordination. Meanwhile, war-torn and poor West Asian countries such as Afghanistan and Yemen, Southern Asian countries such as Pakistan, which are on the verge of imbalance, and most of the countries that are in a state of mild imbalance, such as China, Mongolia, India, Tajikistan, etc., all have poor coupling coordination development of SDGs. From these distribution phenomena, it can be inferred that the coupling coordination development of SDGs is closely related to the economic development level of a country or region. Regions where the coupling coordination development levels of SDGs are higher are mainly concentrated in economically developed Europe, while areas with lower levels are more prevalent in African countries.

4.3. The Impact of Carbon Emissions on the SDG Coupling Coordination Degree among Different Types of Countries

There are significant differences in the SDG (sustainable development goal) coupling coordination degree among 166 countries worldwide (Figure 1 and Figure 2). To examine the driving factors of the SDGs of these countries, and to consider the income level and regional heterogeneity, we used panel regression analysis to further verify and analyze the impact of carbon emissions and other driving factors on the SDG coupling coordination degree.

Carbon emissions have significant promotion effects on the SDG coupling coordination degree in low-income and low–middle-income countries, but significant inhibition effects on those in middle–high-income and high-income countries (Figure 3). This finding suggests that for low-income and low–middle-income countries, reducing carbon emissions is not conducive to the coupling coordination development of SDGs. However, reducing carbon emissions is beneficial for the coupling coordination development of SDGs in middle–high-income and high-income countries. With sound socioeconomic development, the latter countries prioritize healthy and eco-friendly sustainable development methods. By contrast, low-income and low–middle-income countries lack advanced production technologies, and their economic development incurs higher costs than in other countries.

In terms of economic development level, improving it generally helps to better coordinate the development of SDGs. Based on population size, an increase in population would reduce the coupling coordination level of SDGs in low-income and low–middle-income countries (Figure 3a,b). However, an increase in population in high-income countries would effectively improve the coupling coordination level of SDGs (Figure 3d). If a country has a low level of economic development, scarce resources, and poor education, an increase in population would limit human development or even lead to an increase in poverty, thereby decreasing the coupling coordination level of SDGs. The leading advantages of socioeconomic development enjoyed by middle–high-income and high-income countries are undoubtedly advantageous for their coupling coordination development of SDGs in terms of talent attraction and nurturing, infrastructure construction, industrial development, and ecological environment, among others. In terms of the technological level, improving it can promote the coupling coordination development of SDGs. The positive effect of technology varies among countries with different income levels, with middle–high-income countries experiencing the greatest positive effect and low-income countries having the smallest. With regard to international trade level, except for low–middle-income countries, the low regression coefficients of low-income, middle–high-income, and high-income countries are all significantly positive, with coefficient values very close to 0, indicating that improving the level of international trade can also promote the coupling coordination development of SDGs, albeit with a smaller promoting effect than other driving factors. Finally, in terms of economic freedom, the regression coefficients of low-income and low–middle-income countries are positive but have not passed the significance test. An increase in economic freedom tends to promote the coupling coordination development of SDGs in these countries. The regression coefficients of middle–high-income and high-income countries are significantly negative, indicating that an increase in economic freedom would inhibit the coupling coordination development of SDGs. With respect to political stability, except for high-income countries, the regression coefficients of low-income, middle–low-income, and middle–high-income countries are all significantly greater than 0, and the coefficient values gradually decrease, indicating that political stability in these countries is conducive to the coupling coordination development of their sustainable development goals. Political instability in low-income and low–middle-income countries, in contrast to high–middle-income countries adversely affected economic, social, political, and environmental development, hence making political stability all the more essential for the coupling coordination development of SDGs.

Finally, there is regional heterogeneity in the impact of carbon emissions on the SDG coupling coordination degree (Figure 3e). An increase in carbon emissions in Europe, Central Asia, North America, and South Asia would hinder the coupling coordination development of SDGs, whereas an increase in carbon emissions in Latin America and the Caribbean, the Middle East and North Africa, and sub-Saharan Africa would facilitate the coupling coordination development of SDGs. The impact of the East Asia and Pacific regions is yet to be fully manifested. The main reason for this phenomenon may be that most countries in Latin America and the Caribbean, the Middle East and North Africa, and sub-Saharan Africa are relatively underdeveloped or even in a state of extreme poverty. At their current stage, their primary task is to promote socioeconomic development, which can only be achieved to some extent through extensive economic growth patterns with higher carbon emissions, thus making an increase in carbon emissions conducive to the coupling coordination development of SDGs in these regions. This is consistent with the previous finding that an increase in carbon emissions in low-income and low–middle-income countries can promote the coupling coordination development of SDGs.

4.4. Carbon Emissions Have Spatial Effects on the SDG Coupling Coordination Degree

Carbon emissions have significant spatial spillover effects on the SDG (sustainable development goal) coupling coordination degree. Carbon emissions have a significant positive promotion effect on the SDG coupling coordination degree goals in the country itself, but a significant inhibitory effect on the SDG coupling coordination degree in neighboring countries (

Table 6. Coupling and coordinated development of sustainable development goals: spatial effects analysis.

Variable	Main_Coefficient	Wx_Coefficient	LR_Direct	LR_Indirect	LR_Total
LnCE	0.011 *** (0.004)	−0.076 ** (0.037)	0.010 *** (0.004)	−0.139 * (0.076)	−0.129 * (0.077)
$𝜌$	0.518 *** (0.063)
Control variables	Yes
Log-likelihood	5329.278
R2	0.352

Note: * denotes statistical significance at a level of p < 0.10, ** denotes statistical significance at a level of p < 0.05, and *** denotes statistical significance at a level of p < 0.01; the values within parentheses represent standard deviation.

).

The effect decomposition results in Table 6 further support the above conclusions. Under the economic–geographic distance spatial weight matrix, the direct effect of carbon emissions is significantly positive, while the estimated coefficients of indirect effects and total effects are both significantly negative at the 10% level, further indicating that carbon emissions can effectively improve the coupling coordination development of SDGs in the country itself but hinder the coupling coordination development of SDGs in neighboring countries. On the one hand, the positive economic effect generated by increasing carbon emissions masks the negative external environmental impact caused by carbon emissions in the country itself, thereby making an increase in carbon emissions beneficial to the coupling coordination development of SDGs in the country. On the other hand, the economic benefits gained from the increase in carbon emissions in the country itself will not be passed on to neighboring countries, and neighboring countries will be affected by negative externalities such as climate change due to the increase in carbon emissions.

5. Discussion

The negative impact of carbon emissions on the environment is well-known to the public, but the social costs of controlling carbon emissions, particularly their effects on a country’s SDGs (sustainable development goals), have not been thoroughly studied. From the perspective of sustainable development, controlling carbon emissions is advantageous for avoiding negative impacts such as climate change and extreme disasters. However, due to variations in economic development, resource endowments, and geographic location across countries globally, employing extreme political measures to control carbon emissions might jeopardize the coordinated development of sustainable development goals. Therefore, conducting detailed global research on the impact of carbon emissions on the coordinated development of sustainable development goals is crucial.

Through the analysis of data from 166 different countries from 2005 to 2020, we found that the impact of carbon emissions on the coupling coordination development of SDGs varies significantly among different types of countries. For low- and middle-income countries, carbon emissions can help promote the coordinated development of various sustainable development goals. However, for high-income countries, carbon emissions have adverse effects on the coordinated development of various sustainable development goals. This is the first quantitative study that sheds light on the specific impacts of global carbon emissions on the SDG coupling coordination degree.

There is a significant positive correlation between the SDG coupling coordination and a country’s level of economic development (Figure 2 and Figure 3). Therefore, if we want to achieve coordinated development of SDGs, maintaining economic growth and promoting increases in residents’ income levels should remain the long-term development objectives for low- and middle-income countries.

Reducing carbon emissions remains an important challenge for global governance. Excessive carbon emissions will lead to serious climate change problems and hamper the sustainable development of society as a whole [68,69]. However, to a certain extent, an increase in carbon emissions reflects an increase in economic production activities, and economic growth in low-income countries can effectively promote employment, increase income, and reduce poverty rates over a period of time [70]. For low- and middle-income countries, restricting carbon emissions may slow down economic growth and incur significant social development costs, which is further supported by the results of our study.

Therefore, it is necessary to develop different carbon reduction policies based on the situation of each country to avoid imposing carbon emission quotas on countries through a one-size-fits-all approach, which would aggravate inequality among countries. For high-income countries, reducing carbon emissions can promote coordinated development between society, the economy, and the environment. Reducing carbon emissions is clearly more advantageous for these types of countries, which also tend to have greater international influence. Finding a way to balance the development differences among countries and develop effective carbon reduction policies will be an important challenge for international policymakers.

Strengthening cooperation among countries, especially those that are geographically adjacent, will be an important means of controlling carbon emissions and achieving coordinated development of SDGs in the future. International cooperation has been proven by most scholars to be effective in promoting carbon emission reduction [71,72]. Our study found that carbon emissions can promote the coupling coordination of a country’s own sustainable development goals, but they impose negative externalities on the coupling coordination of neighboring countries’ sustainable development goals. Through international cooperation, particularly regional international cooperation, these negative externalities can be mitigated to some extent.

In our study, we faced a dilemma of data and country selection. We chose 90 indicators to evaluate the SDG coupling coordination degree in 166 countries. Although it would be better to use more indicators to evaluate the SDG coupling coordination degree, this is already the limit of available data for 166 countries. If more indicators were used, the number of countries with complete data would decrease sharply, making it impossible to conduct research on low- and middle-income countries, and the research conclusions may not be constructive. This choice may be the best solution given the lack of statistical data.

6. Conclusions

Carbon emissions, as a direct output of energy consumption and an important driver of economic growth, are the key issue faced in tackling global climate change and achieving SDGs. For developing countries, controlling carbon emissions may harm economic growth and bring about a range of social development issues [25]. For leaders of these countries, controlling carbon emissions will be a difficult choice. For developed countries, controlling carbon emissions is advantageous for sustainable development and provides incentives to continue promoting carbon reduction initiatives at the national level [10,73]. In order to avoid national-level inequality resulting from reducing carbon emissions, international policymakers need to consider the interests of all countries. From the perspective of social impact, this study quantitatively evaluates the specific effects of carbon emissions on the coordinated development of sustainable development goals in various countries worldwide. Our research findings provide crucial evidence for the formulation of future international policies aimed at reducing carbon emissions.

• Globally, the coupling coordination development level of SDGs has been generally low during the period from 2005 to 2020. Despite gradual improvement, the speed of progress has been slow and has remained in a transitional development state of barely coordinated disorder. From the perspective of the different income levels of countries, the level of coupling coordination development of SDGs during the examined period varied significantly, showing a trend with high-income countries exhibiting higher development levels followed by middle–high-income, middle–low-income, and finally low-income countries.
• Spatially, the level of coupling coordination development of SDGs among the 166 countries has made significant progress during the same period, with countries demonstrating a step-wise increase in their respective sustainable development levels, and the area of development growth expanding further. Specifically, with the exception of certain African countries still in a polarized stage, North America, South America, Europe, Asia, and Oceania have all basically entered an adaptive phase. The level of sustainable development coordination varies greatly across different regions, with better levels of coordination mainly found in economically developed Europe, and lower levels of coordination concentrated in poor and underdeveloped Africa, exhibiting a spatial distribution characteristic of “low in Africa, high in Europe”.
• Carbon emissions have a significant promoting effect on the coupling coordination development level of SDGs among low- and low–middle-income countries, but a significant inhibitory effect on those of middle–high- and high-income countries. Economic development level, population, technological progress, international trade, economic freedom, and political stability are also important driving factors affecting the coupling coordination development of SDGs. In addition, there exists regional heterogeneity in the impact of carbon emissions on the coupling coordination development level of SDGs.
• Carbon emissions have significant spatial spillover effects on the coupling coordination development level of SDGs. Carbon emissions have a significant positive promoting effect on the domestic coordinated development level of SDGs and a significant inhibitory effect on the coupling coordination development level of neighboring countries.