Analyzing the Progress of China and the World in Achieving Sustainable Development Goals 7 and 13

Abstract

Achieving Sustainable Development Goal 7 (SDG 7) and SDG 13 together requires a holistic and integrated approach to simultaneously address the challenges of clean energy and climate action. In order to find integrated policy strategies, this study offers a comparative analysis using the case of China and the world regarding energy access, energy intensity, clean cooking, renewable energy, global warming gases, and investment in energy by the private sector to advance SDGs 7 and 13, applying a principal component regression (PCR) and forecasting models for the period 1990 to 2021. Overall, these findings indicate that China is making significant progress towards meeting the goals of the Paris Agreement. This progress is evident in the notable variations observed in key variables such as access to clean cooking solutions, private sector investments in energy, renewable energy generation, and enhanced energy efficiency. In contrast, the global landscape exhibits only minimal fluctuations in these aspects within its framework. The PCR proves that all the components are significant regarding China, whereas, for the world, seven components are significant out of eight. Furthermore, the global temperature projection indicates that the world is nearing the 1-degree Celsius threshold, with the current temperature standing at 0.558 degrees Celsius. This suggests that the goal of limiting global warming to 1.5 degrees Celsius by 2030 remains attainable. Notably, China’s projected average temperature for 2030 is 7.2 degrees Celsius, marking a 12% decrease from the 2021 temperature level. This trajectory aligns with China’s commitment to achieving the 1.5-degree Celsius target by 2030. This study makes a valuable contribution to the field of energy transition, offering insights into the path to maintaining global warming at 1.5 degrees Celsius as stipulated by the Paris Agreement by 2030.

1. Introduction

Global warming since the pre-Industrial Revolution has been a challenge confronting world leaders. Government policymakers over the years have implemented consolidated policies, including the Millennium Development Goals (MDGs), Sustainable Development Goals (SDGs), and consequently, the Paris Agreement (PA), to control greenhouse gases (GHGs) [1]. Among the SDGs, SDG 7 and SDG 13 play a crucial role in achieving the PA, which was adopted by 196 countries under the United Nations to work to limit the rise of the global temperature below 2 degrees Celsius compared to pre-industrial levels. SDG 7 denotes the availability of affordable, clean, reliable, modern, and sustainable energy for all by 2030, while SDG 13 demonstrates that action is needed to combat climate change. China has been a significant global energy consumer, accounting for 21% of the cumulative total and responsible for almost 30% of energy-related emissions [2]. Consequently, in 2016, China became a signatory to the PA. Under this accord, China committed to several key objectives in its Nationally Determined Contributions (NDCs). These commitments include the goal of curbing carbon dioxide emissions by 2030, increasing the proportion of low-carbon resources in its energy mix to 20% by 2030, and reducing carbon dioxide emissions per unit of gross domestic product (GDP) by 60% to 65% compared to 2005 levels [2].

Following the adoption of the SDGs and PA, many countries, including China, have made progress in achieving universal access of sustainable energy. Nevertheless, it remains a stark reality that approximately 733 million people globally still lack access to energy, while 2.4 billion people continue to lack access to modern cooking solutions. Recent research on this field has gained traction among scholars who found renewable energy consumption (REC) increased to 17.7% of total consumption in 2019, with the electricity sector forming a larger share of 26.2% of total final energy consumption [3,4,5]. The heat and transport sectors show significant potential for the penetration of renewable energy (RE). Furthermore, global primary energy intensity, which is the world’s cumulative supply of energy per GDP, increased by 5.6 megajoules per dollar in 2010 to 4.7 megajoules [6]. Furthermore, the global movement of finances to support the integration of RE reached a figure of USD 10.19 billion in 2019, about 23.6% less than what was attracted in 2018. The literature suggests that China has, however, achieved universal electricity access and has increased access to clean cooking solutions by an average of 2% since 2016 [7]. Rising global temperatures are estimated to impact the availability of essential livelihoods, food security, and energy [7]. The least developed countries are already bearing the brunt of climate change, due to their inability to mitigate and adapt to the worse consequences of climate change, which is impacting their socioeconomic and natural resources development. In view of the aforementioned, there is a strong relationship between climate change and sustainable energy. Therefore, achieving SDG 7 and SDG 13 together requires an all-inclusive and integrated approach to simultaneously address the challenges of sustainable energy and climate action [8]. In this integrated approach, the SDG13 aims to take urgent action in combating climate change and its impacts while attaining the United Nations Framework Convention on Climate Change (UNFCCC), which serves as the primary global, multilateral platform for discussions on how the world will address the challenge of climate change [9].

In examining the SDGs, several scholars, including Tang et al. [10], conducted a comprehensive assessment of air quality and the co-benefits associated with China’s carbon peak. Additionally, Zhang et al. [9] utilized a nonlinear autoregression distributive lag (ARDL) approach to determine whether technological innovation hindered or enhanced the sustainability of China’s ecology from 1990 to 2019. Moreover, Zhou et al. and Miao et al. [11,12] applied the SDG framework to investigate sustainability at the city level in China and compared the carbon neutrality trajectories of China and the United States. In broadening the understanding of the SDGs studies and seeking to contribute to the existing literature, this study aims to provide a comparative analysis of the progress of China and the global progress in achieving SDGs 7 and 13. To accomplish this broad objective, we established the following three specific objectives in the analysis: (1) Conduct a comparative assessment of the progress made by China and the world in achieving SDGs 7 and 13 using the principal component regression (PCR) method. (2) Forecast the temperature trends for China and the world to assess the feasibility of reaching the 1.5-degree Celsius target by 2030. (3) Offer policy recommendations based on study analysis and suggest further actions to advance the goals of SDGs 7 and 13. In our contribution to the existing body of research, we employ principal component regression (PCR) to reduce the complexity of the factor structure while linearly estimating the principal components (PCs). Furthermore, we use this technique to forecast global progress in terms of total greenhouse gas emissions and SDG 13, utilizing annual mean temperature data to determine whether the world and China are on track to meet the PA’s target of limiting global warming to within 2 degrees Celsius. Our findings indicate that China is making substantial progress towards achieving both SDG 7 and SDG 13, as evidenced by the significant estimated PCs. In contrast, the global community lags behind in these efforts. Specifically, the results show that the world is approaching the 1-degree Celsius threshold, while China’s projections for the PA target indicate a temperature decrease of 12%, with a forecasted mean temperature of 7.2 degrees Celsius by 2030.

This study is structured as follows: Section 2 reviews the literature and Section 3 presents the data and methods applied in the analysis to arrive at the results. Section 4 discusses the results, and finally, Section 5 presents the conclusions.

2. Review of the Literature

Global temperature rise has profound implications on essential aspects of human existence, including food security, water resources, and energy supply. In 2015, the PA marked a significant milestone in the global effort to address these pressing challenges of climate change. The PA called upon member countries to develop and implement localized strategies and plans aimed at both mitigating and adapting to climate change. The PA primary objective was to prevent global temperatures from exceeding 1.5 degrees Celsius above pre-industrial levels by the year 2030. In this regard, several studies in the literature, including works by Kuhn and Dong et al. [6,13], have emphasized the intricate connection between domestic factors influencing climate change and the broader global dynamics, underscoring China’s pivotal role in the transition to green energy. Additionally, research by Finance et al. [14] has shed light on China’s financial sector, with a particular focus on the China Construction Bank (CCB), highlighting the commitment of the Chinese private sector to principles of green finance. This commitment underscores the importance of private sector investment in China’s energy transition. Bai et al. [15] have echoed the importance of investing in renewable energy (RE) as a means to advance sustainable recovery, reduce greenhouse gas emissions, and accelerate a more resilient plan for China’s economic recovery. Furthermore, Lacobuta et al. [8] discovered the beneficial impact of climate-related official development assistance (ODA) on the achievement of various SDGs, particularly SDG 7. It is worth noting that China plays a significant role in global green investment, accounting for more than half of such investments worldwide. In 2019 and 2020, global climate finance investments totaled approximately USD 479 billion, with approximately USD 292 billion directed towards the East Asia and Pacific (EAP) region. China, in particular, attracted roughly one-fourth of this investment. This substantial influx of capital has been reflected in China’s recent reductions in emissions [14,16]. As a result, research by de Assis Tavares al et al. [17] has highlighted the ways in which climate change impacts China’s economy, especially in the agriculture sector, where it has explicit and implicit effects on food production processes. That is, direct impacts on food affect the availability of carbon dioxide, precipitation, and temperature fluctuations. In contrast, indirect impacts are related to seasonal variations and availability of water resources.

Similarly, WB et al. [18] found that even though China has made substantial progress in poverty reduction, climate change has exacerbated income disparities among the populace. Between 1978 and 2015, the share of people who are top earners in national income accounting in the 10% bracket increased from 27% to 41%, whereas the share of people who hold the bottom 50% earners fell from 27% to 15%. About 11.4% of Chinese live in low-lying coastal areas, exposing them to climate disasters. Similarly, according to Abudu et al. [19], achieving the NDCs poses a significant challenge for many developing countries due to their heavy reliance on fossil fuel sources for energy consumption. Zhang et al. [20] draw attention to the synergetic interaction of the SDGs by evaluating the spatial and temporal interactions of the SDGs in China by systematically grouping the SDGs into thematic areas such as “Essential”, “Objective”, and “Governance”. The literature discovered synergies at the provincial level among Western and Central provinces, denoting that these provinces are on a sustainable development trajectory. Furthermore, in the short run, they found that the synergies of these thematic SDGs demonstrated fragility in the last ten years, mainly because of the regional differences in progress in the achievement of SDG 7. Moreover, Matenga et al. [21] emphasize the importance of achieving SDG 7 using an unsupervised machine learning method (ordinal K-Means Clustering). Matenga and Tete et al. [21,22] acknowledged the poor performance of energy markets in Sub-Saharan African countries in terms of access to energy, as well as concerns about energy security and low levels of sustainability in advanced economies such as the United States and a rapidly developing economy, China. Highlighting the worldwide energy access gap, Onu [23] discovered that approximately 679 million people across the globe will still lack access to energy by 2030. Furthermore, Burke [24], demonstrated that efficiency alone does not guarantee the achievement of SDG 7. This is due to the “take-back effect,” and it is energy sufficiency that ensures meeting mandatory overall economic budget or sufficiency limits on total energy consumption before considering lifestyle adjustments and the deployment of renewable energy sources.

Tetel et al., Onu, Alemzero et al., and Alemzero et al. [3,5,22,23,25] argued that improved energy efficiency reduces energy intensity levels. Ref. [26] contended that, for China to achieve green growth, it must increase the deployment of renewables. According to Meidan [27], China can achieve its carbon neutrality much earlier than 2060, as announced by President Xi Jinping in September 2020 at the UN meeting. The building block to achieve this is to decarbonize the national grid by generating power from a low-carbon source. A seminal study by He et al. [28] revealed that the mean levels of energy consumption and carbon emissions in China have fallen by 6.0 percent and 5.4 percent in 2005–2013 to 2.2 percent and 0.8 percent in 2013–2018, respectively. Furthermore, China’s new 14th five-year plan expects the GDP growth rate to be around 5%, the energy intensity of the economy to be around 14 percent, and the use of renewable energy to increase by 7 percent annually [28]. A study by Kap Lu et al. [29] drew our attention to challenges facing the attainment of SDG 7 in the world, such as reducing the consumption of conventional energy plus expanding the energy mix to more clean technologies and many others. They found solutions in their work to achieve SDG 7 by stating that there is a need to scale up investment and grow the consumption of clean technologies. Regarding China, X. Liu and M. Yuan [30] constructed eleven transport indicators within the United Nations framework, where the scores were estimated alongside the spatio-temporal patterns and interactions. Their findings discovered that China’s transportation sector scores were very high (i.e., scores above 75). Their interaction analysis revealed three indicators with scores higher than 0.5, and indicators also showed mixed correlations directly and indirectly, meaning the development of sustainable transport in China. This is because all public buses that run on the road in China are electrified. Furthermore, the use of electric cars is increasing in China day by day. Most of these are made by China’s domestic brands such as LEPMOTORS, NIO, and Build Your Dream (BYD), companies that make the transport sector emit less carbon dioxide and, therefore, cause China to contribute to achieving SDG 7. In analyzing the 176 countries’ performance regarding SDG 7, Gebara et al. [31] formulated a framework with 29 indicators covering environmental and socio- and techno-economic parts vital to the sustainability of the energy sector. They discovered from their analysis that virtually all the countries had performed poorly concerning most of the indicators, but with varied scores, and with environmental scores exactly above the upper limits and others surpassed by a factor of a thousand. Fifty-two countries have performed below their ceilings regarding climate effects, and these countries are found in Africa and Asia.

Subsequently, Zhao et al. [32] developed a composite index of SDG 7 with the utilization of “STAR” (Straightforwardness, Transparency, Availability, Readiness) concerning universal energy sustainability. Their results indicated that most countries around the globe are on the way to universal coverage of energy propositions, but Sub-Saharan African countries are lagging behind. Furthermore, SDG 7 scores varied among the countries with 8.6% (20 out of 232) in the globe belonging to the high-performing countries and 34% (74 out of 232) in the middle level. This implies that the STAR approach is very important to advance the course of SDG 7. The achievement of SDG 7 will hinge on financing. Likewise, [33] stated that the energy supply banking ratio (ESBR) is in accordance with real economy financing of most economies of 0.9:1. Banks’ financing for energy totaled USD 1.9 trillion. Of that amount, USD 842 billion was invested in low-carbon sources and companies, and USD 1038 billion was invested in conventional energy sources. Ultimately, Razzap et al. [34] analyzed and modeled buildings using the REVIT models and concluded that meeting the economic and environmental needs led to a reduction in carbon dioxide of 32.8 metric tons per annum due to re-fitting, and the building envelope and electrical appliances have a payback period of 2.96 and 2.62, correspondingly, while the suggested solar system has a payback period of 2.3 per annum. Important among their findings was that the buildings were categorized using the leadership in energy and environmental designing tool and attained Silver for certification after the coupling of renewables. This showed the importance of retrofitting and coupling of renewables in achieving SDG 7. This is observed in China’s massive deployment of offshore wind and onshore wind energy technologies, leading to the curtailment of electricity generated due to grid constraints of more than 12% [35]. Another observation by EEH [36] confirmed that access to a clean cooking solution is central to achieving SDG 3 (good health and wellbeing), SDG 5 (gender equality), SDG 7 (affordable energy for all), SDG 13 (climate action), and SDG 15 (life on land). A seminal study by Hu et al. [37] concerning the adoption of modern cooking solutions revealed that the kitchen intensity levels of PM_{2.5 PAH}, NO, and NO₂ are greater when cooking with natural gas than when cooking using electricity, regardless of what one is cooking. This implies that cooking with low-carbon resources is very important to cutting emissions levels.

The literature review presents a gap in SDGs 7 and 13, which have not been studied simultaneously in the context of China with an emphasis on comparison at the global level. Research gaps exist in understanding how SDGs 7 and 13 interact with other SDGs, such as those related to health, poverty reduction, and economic development. More studies are needed to explore the interconnections and trade-offs, yet there exists a lack of reliable data on energy access, emissions, and climate impacts. In conclusion, closing these literature gaps may contribute to a more comprehensive and holistic understanding of the challenges and opportunities related to SDGs 7 and 13, ultimately supporting more effective policy and action in these critical areas.

3. Data and Materials

The primary objective of this study is to assess China’s advancement toward attaining SDG 7 and SDG 13 in comparison to global progress. Based on the research objectives, as explained in the introduction section, the authors adopt the PCR analysis technique. One notable advantage of PCR analysis is its ability to avoid the issue of endogeneity. Moreover, it dissects the complex factor structure into individual variables, thereby facilitating the identification of the most influential factors affecting progress toward the goal of achieving a 1.5-degree Celsius target by 2030. PCR offers another advantage over multivariate methods in that it conducts eigenvalue decomposition just once on the predictors before making predictions for all variables in the regression.

Table 1. Variable definitions.

Variable	Definition	Measurement
Energy use (KG of oil equivalent per capita)	Energy use refers to the use of primary energy before transformation to other end-use fuels.	KG of oil equivalent per capita
Access to clean fuels and technologies for cooking.	Denotes people’s access to modern cooking solutions	Percentages
Renewable energy consumption (% of the total final energy consumption)	Denotes the amount of RE consumed in final consumption	Percentages
Investment in energy with private participation (in US$)	Explains investments in RE by the private sector.	Percentages
The primary energy intensity level of primary energy (MJ/$2017 PPP GDP)	Energy intensity denotes the amount of energy used to generate a percentage of GDP.	MJ/$2017 PPP GDP
Total greenhouse gas emissions (% change from 1990)	Explains the cumulative number of gasses emitted from global warming.	Percentages
Access to electricity	Denotes the number of people that have access to electricity.	Percentages

Source: World Bank Development Indicators.

presents a comprehensive compilation of variables, complete with their respective definitions and methods of measurement. The choice of sample size in this study was based on data availability and the rationale behind variable selection. Furthermore, the selected variables serve as key indicators for measuring progress toward achieving SDGs 7 and 13. According to IEA et al. [38], these are primary indicators of China and the world for the attainment of SDGs 7 and 13. The study, however, excluded other macro variables that are not SDGs 7 and 13 indicators, as mentioned in [38]. This study’s variable units were selected based on their importance in achieving SDG 7, which is central to achieving the PA target of limiting global temperature to an increase of 1.5 degrees Celsius. China is the biggest emitter by volume and the second-biggest economy in the world. Even though China has achieved universal access to electricity, the country still lags in access to clean cooking solutions. About 296 million people lack access to modern cooking solutions in 2023, and China is one of the countries that has improved its energy intensity sufficiently since 2010 to achieve SDG 7 [38]. This energy intensity reduction may impact economic development [39]. The world, on the other hand, has not fully achieved electrification, and access grew by 0.7% points between 2001 and 2021, increasing from 84% of the global population to 91%, and increasing the number of people with access to electricity to a billion. Over the decade, access to electricity has advanced remarkably, reducing the number of people without access to electricity in 2010 from one billion to 657 million in 2021 [38]. The number of people without access to clean cooking solutions is currently 2.3 billion, and renewable energy consumption increased 19.1% as a proportion of total consumption. Furthermore, energy intensity improved to 4.63 MJ/ USD of primary energy intensity [38]. Given this background, this study seeks to compare China’s and the world’s progress in achieving SDGs 7 and 13. The data analysis was performed using the Stata 17 statistical software package. Data were derived from the World Bank Development Indicators (WDI).

3.1. Model

The study uses PCR, which is modeled below, according to Alemzero et al., Hadi et al., and Massy [25,40,41]. Let $y\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{t}\mathrm{e}$ the explained parameter and $x$ represent the formulated matrix representing independent variables. Because we want to maintain the variable in $x$, we standardized the components using the Z-score, which is a prerequisite to carrying out the principal component analysis. The Z-score normalizes the values to a common mean. It is given below.

$$Z=\frac{x-\mu }{\sigma }$$

(1)

where $Z=standard$ score, $X$ = Observed values, $\mu$ = mean of the sample, $\sigma =standarddeviation$ of the sample.

Then, we computed the principal components. Let ${\lambda }_1\ge \dots \ge {\lambda }_p$ as the eigenvalues of $Z^{T}Z$ and $V$ as the matching eigenvectors. Let W $=ZV$. The columns in $W$ are the principal components of $Z$. The $j^{th}$ column of $W$ is designated as $j^{th}$ principal components $j=1,\dots ,p$.

We then subsequently estimate $y$ on the first $m$ principal components, $w_1\dots w_m$, where $m\le p$.

Furthermore, as inferred with the above information, the model is formulated linearly by substituting the exploratory variables with PCs, as depicted below. We then fit the model below with the dependent variables (i.e., energy use).

$$Eneu=f(Ackcg,REC,EI,GHGs,IEPPC,ELECT)$$

(2)

$${Eneu}_i={\beta }_0+{\beta }_{1}PC1i+{\beta }_{2}PCi+{\beta }_{3}PCi+{\beta }_{4}PCi+{\beta }_{5}PCi+{\beta }_{6}PCi+{\beta }_{7}PCi+{\beta }_{8}PCi+{\epsilon }_{it}$$

(3)

One of the advantages of using PCR is that due to the fact that the PCs are orthogonal, the issue of multicollinearity is resolved. Additionally, the PCR is believed to present robust and advanced results because of its orthogonality to PCs.

3.2. Theoretical Forecasting Model

Equation (4) below represents the forecast model as performed in Hendry [42]. Hendry noted that time series are subject to rare changes [42]. The change prediction could be improved by extrapolating the current temperature at the most current rates.

$$\widehat{S_t}+\mathrm{h}/\mathrm{t}=S_t+1+△S_t^{h}h=1,3,6,9,12$$

(4)

where $△S_t^h$ represents the percentage growth rate between $t-1$ and t.

Figure 1 outlines the methodology. The Z-score normalizes the data before analysis according to the needs of the PC analysis. PCR analysis breaks down the factors into its component structure before analysis. The PCR performs the analysis with a stochastic term by capturing the unobserved terms, and the scree plot of the eigenvalue, which represents the loading of the variables necessary for the analysis. Finally, the global warming gases forecasts as well as the global and China temperature forecasts are derived and represented using plots and analysis.

4. Results and Discussions

This section provides an overview of the study’s results, which were analyzed using the Stata 17 statistical package with reference to the work of Mistry et al. and Reiss et al. [43,44].

Table 2. Descriptive statistics values for China and the rest of the world.

Variable	Obs	Mean	Std. Dev.	Min	Max
Year	32	2005.5	9.381	1990	2021
ENEU_C	32	1001.139	711.219	0	2224.355
ACKCG_C	32	50.827	37.835	0	89.4
REC_C	32	20.109	9.906	0	34.084
IEPP_C	32	1.96 × 109	1.50 × 109	0	5.95 × 109
EI_C	32	5.521	4.523	0	10.85
GHGS_C	32	54.613	69.306	0	219.952
ENEU_W	32	110.986	60.840	0	164.129
ACKCG_W	32	38.053	28.468	0	69.682
EI_W	32	4.927	3.185	0	7.964
GHGS_W	32	11.371	13.112	−0.4	40.003
REC_W	32	14.049	6.868	0	18.13
ELECT_W	31	61.511	37.094	0	90.522

Note: _C is for China; _W is for World. Source: Authors’ estimation.

presents the descriptive statistics for both China and the rest of the world. One notable finding is that private sector investment in energy (IEPP_C) achieves the highest mean, aligning with the study by [14], which indicated that China attracted a significant portion of climate investment in the EAP region in 2019/2020, accounting for approximately one-fourth of the total. The variable with the second-highest mean is energy use in China (ENEU_C). In contrast, the same variable for the global scale (ENEU_W) boasts the highest mean value, which is nearly nine times greater than that of the rest of the world. This suggests a substantial difference in energy consumption patterns between China and the global average. Regarding access to clean cooking solutions, the indicator for urban access in China (ACKCG_C) ranks third in terms of mean value. This underscores the fact that universal access to clean cooking solutions has not been achieved in China, with an urban access rate of just over 80%. In comparison, the indicator for the rest of the world (ACKCG_W) obtains the second-highest mean globally. When comparing China and the rest of the world, it becomes evident that China has made more significant progress in this regard. This finding aligns with the study by Nix et al. [45], which reported a 40% increase in energy used for cooking in 2020. It is noteworthy that, despite China’s relatively high urban access rate to clean cooking solutions, the global situation remains challenging, with around 33.4% of the world’s population lacking access, equivalent to 2.4 billion people without electricity for clean cooking solutions [45].

The (EI_C) in China stands notably higher than the global average, at 5.521, indicating a relatively high carbon intensity in the Chinese economy. In contrast, the world’s energy intensity (EI_W) is 4.927, suggesting a comparatively lower carbon intensity on a global scale. This highlights China’s position as a high carbon intensity economy. Furthermore, China’s rapid economic development has led to a substantial production of greenhouse gases (GHGs_C), with an average figure of 54.613, which significantly surpasses the global average of 11.371. This underscores China’s status as the largest global emitter of greenhouse gases in terms of volume. In terms of (REC_C) as a percentage of total consumption, China demonstrates a notable commitment, with a proportion of 20.109%, compared to the global average of 14.049%. This reinforces China’s position as a leader in the deployment of renewable energy. Lastly, it is important to note that China has achieved full electrification, ensuring widespread access to electricity, while globally, full electrification (ELECT_W) has not been realized. Worldwide, approximately 675 million people still lack access to energy, and it is estimated that around 700 million will continue to lack access by 2030 [23].

The correlation matrix in

Table 3. Matrix of correlations.

Variables	ENEU_C	ACKCG_C	REC_C	IEPP_C	EI_C	GHGS_C
ENEU_C	1
ACKCG_C	0.136	1
REC_C	−0.157	−0.859	1
IEPP_C	0.116	−0.099	0.007	1
EI_C	0.413	0.818	−0.501	−0.056	1
GHGS_C	0.7	0.303	−0.343	0.05	0.495	1
Variables	ENEU_W	ACKCG_W	EI_W	GHGS_W	REC_W	ELECT_W
ENEU_W	1
ACKCG_W	−0.636	1
EI_W	0.884	−0.683	1
GHGS_W	0.332	0.216	0.388	1
REC_W	0.878	−0.476	0.732	0.35	1
ELECT_W	−0.528	0.883	−0.572	0.253	−0.36	1

Note: _C is the China; _W is World. Source: Authors’ estimates.

reveals several key insights. Notably, China’s (REC_C) and access to (ACKCG_C) exhibit the highest negative correlation. This correlation arises from the limited use of renewable energy for cooking purposes in China, as it is predominantly employed for power generation. However, the strength of this correlation underscores the significance of these variables in the pursuit of SDG 7 and SDG 13. As renewable energy deployment scales up, resulting in lower prices, it becomes increasingly crucial for widespread adoption and utilization. Additionally, there is a noteworthy relationship between China’s (EI_C) and (ACKCG_C). Furthermore, EI_C and REC_C exhibit a significant negative correlation, suggesting that as renewable energy consumption increases, energy intensity decreases. This implies a positive impact of renewable energy deployment on reducing carbon intensity. In contrast, there is a positive yet statistically insignificant correlation between (IEPP_C) and greenhouse gas emissions (GHGs_C). This suggests that private sector investment in energy is associated with lower emissions levels in China, possibly due to government control over a significant portion of the energy sector’s activities. Moreover, GHGs_C and (ENEU_C) demonstrate a positive correlation, indicating that higher energy consumption, particularly when the energy mix is skewed towards fossil fuels, leads to increased emissions. This aligns with the observation that China’s investment in energy has resulted in recent emissions reductions [5]. On a global scale, (ENEU_W) and access to clean cooking solutions (ACKCG_W) show an indirect negative relationship, reflecting an inverse correlation between these variables. Conversely, there is a significant positive relationship between (EI_W) and (ENEU_W), reinforcing the earlier analysis and suggesting that energy intensity worsens when conventional energy sources dominate the energy mix. Furthermore, REC_W and ENEU_W demonstrate a significant relationship, affirming that increased energy use underscores the need for sustainable renewable energy sources. Notably, (GHGs) correlations are less significant globally, reflecting lower global emissions levels compared to those of China. Lastly, the (ELECT_W) exhibits a significant relationship with access to clean cooking, renewable energy, and energy use, highlighting the importance of complete electrification in achieving SDG 7 on a global scale.

Table 4. Principal components/correlation.

Component	Eigenvalue			Difference			Proportion			Cumulative
Comp1	5.632			1.94			0.469			0.469
Comp2	3.692			2.619			0.308			0.777
Comp3	1.073			0.381			0.089			0.866
Comp4	0.692			0.171			0.058			0.924
Comp5	0.521			0.325			0.043			0.967
Comp6	0.196			0.056			0.016			0.984
Comp7	0.14			0.102			0.012			0.995
Comp8	0.037			0.026			0.003			0.999
Comp9	0.012			0.006			0.001			1
Comp10	0.006			0.006			0.001			1
Comp11	0			0			0			1
Comp12	0						0			1
Principal components (eigenvectors)
Variable	Comp1	Comp2	Comp3	Comp4	Comp5	Comp6	Comp7	Comp8	Comp9	Comp10	Comp11	Un explained
ZENEU_C	−0.049	0.449	0.12	0.223	−0.487	0.132	−0.681	−0.063	0.098	0.021	0.003	0
ZACKCG_C	0.39	0.162	−0.041	0.176	0.169	0.067	0.08	−0.252	0.332	−0.3	−0.697	0
ZREC_C	−0.381	−0.117	−0.109	0.102	0.39	0.217	−0.262	0.526	0.462	−0.245	0.012	0
ZIEPP_C	−0.041	0.053	0.915	−0.221	0.293	0.1	−0.056	−0.092	0.006	−0.032	0.004	0
ZEI_C	0.239	0.341	−0.109	0.306	0.544	0.207	−0.139	0.179	−0.496	0.29	0.008	0
ZGHGS_C	0.043	0.453	−0.067	−0.438	−0.228	0.49	0.344	0.312	−0.113	−0.275	0.006	0
ZENEU_W	0.41	0.02	0.141	−0.026	−0.157	−0.071	0.138	0.447	0.44	0.606	−0.023	0
ZACKCG_W	0.395	0.15	−0.041	0.162	0.152	0.058	0.086	−0.25	0.338	−0.263	0.717	0
ZEI_W	−0.358	0.192	−0.214	−0.241	0.197	0.323	0.062	−0.497	0.302	0.497	−0.009	0
ZGHGS_W	0.007	0.432	−0.154	−0.482	0.242	−0.666	−0.202	0.068	0.065	−0.071	0	0
ZREC_W	−0.305	0.306	0.115	0.358	−0.038	−0.202	0.353	0.045	0.051	0.001	0.004	0
ZELECT_W	−0.305	0.306	0.115	0.358	−0.038	−0.202	0.353	0.045	0.051	0.001	0.004	0

Note: _C is China; _W is World. Source: Authors’ estimation.

provides the component correlations essential for data analysis, revealing the necessity of utilizing three distinct components due to their multiple associations with the variables. It also elucidates the extent to which each variable contributes to the variation within each component. In the context of China’s analysis, it becomes evident that ENEU_C significantly accounts for a substantial portion of the variation within most components. Additionally, IEPP_C plays a pivotal role in explaining a considerable degree of variation, underscoring the significance of private sector investment in energy [46,47]. Similarly, total GHGs_C emerges as a critical factor, explaining a significant percentage of the variation in the Chinese component, reaffirming China’s position as the largest emitter in terms of volume [48,49,50]. Conversely, on a global scale, REC_W elucidates only a limited degree of variation within the components, reflecting the relatively low deployment of renewables worldwide. Similarly, ACKCG_W [45] also explains minimal variation, pointing to challenges in achieving universal access to clean cooking solutions globally.

Table 5. PCA linear regression.

Component	Model 1	Model 2
PC1	−0.0479 ***	0.398 ***
	(−50.37)	−67.87
PC2	0.441 ***	0.0197 *
	−376.07	−2.72
PC3	0.118 ***	0.137 ***
	−54.05	−10.18
PC4	0.219 ***	−0.0256
	−80.88	(−1.53)
PC5	−0.478 ***	−0.152 ***
	(−153.02)	(−7.88)
PC6	0.130 ***	−0.0690 *
	−25.44	(−2.19)
PC7	−0.669 ***	0.134 **
	(−110.85)	−3.61
PC8	−0.0618 ***	0.433 ***
	(−5.30)	−6.03
_cons	0.0454 ***	−0.0533 ***
	−20.47	(−3.90)
N	31	31 R2 = 99.5%

Note: t statistics in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001; Source: Authors’ estimations.

computes the scoring coefficients for the components and then estimates eight PCs. Model 1 presents the linear results regarding China, where PC1 is negatively associated with ENEU_C, implying that PC1 decreases energy use by 4.7%, and energy use decreases by one percentage. However, PC2 is positively correlated with ENEU_C. This denotes that PC2 increases energy use by 44.1%, if there is a percentage increase in energy use. All PCs are significant regarding China, illustrating the significant progress of China in achieving SDG 7 and SDG 13. The derived R-squared explained 99% variation within the model, depicting the model fitness in estimating these predictors. On the contrary, on the model estimating the global parameters, PC2 is directly linked to ENEU_C, as well as PC3, PC5, PC6, PC7, and PC8, implying that these components greatly influence the outcome of the results. The R-squared value also explained more than 99% of the variation within the model, depicting the model fitness of the analysis.

Figure 2 shows the component loadings of the variables. The variables seem to be evenly distributed between the two components. As depicted, ZENEU_C, ZGHGs_C, and ZGHGs_W are outlier variables. These outliers explain why these variables are the highest in China and the world. ZEI_C, that is, energy intensity for China, loaded strongly in both components, but China is a little higher than the global EI levels. The IEPP is in the middle for China, highlighting the steady increase in attracting climate investment. The ACKCG loaded strongly for both China and the globe, but China’s access explains urban access, not national access. Given this reason, China has done well in deploying clean cooking solutions.

Figure 3 displays the scree plot of eigenvalues after PCA. The figure shows that three components are important for the analysis, as depicted in Table 4. The Kaiser rule of thumb requires components with eigenvalues of more than one to be used for the analysis. The mean line intersects through the eigenvalues line, showing that at least three eigenvalues are necessary for the analysis. Therefore, these components greatly influence the factor structure in the analysis.

Figure 4 is a visual representation of the forecast of total GHGs for the world (a) and China (b). The essence of performing the forecast is to determine the trend and the rate of reduction or otherwise of GHGs for the study countries to achieve the Paris Accord. On the left side, global GHGs have been on the increase since the 1990s, peaked in the 2000s, and dipped considerably in 2019 as a result of the novel coronavirus disease-19 (COVID-19), as the figure shows a flat trend. Global GHGs would have increased by 10% in 2030, the forecast period shows, depicting that global GHGs are growing yearly. On the right-hand side, the forecast for GHGs for China would increase by about 48% by 2030. This is due to the fact that China is the largest emitter by volume. The trends of the two forecasts are quite similar. These forecasts are in line with several studies, such as [6,51]. According to Bureau [52], GHGs emissions by 2030 will have to be nearly 25% and 55% less than 2017 levels for the world to have the chance of limiting global temperature between 2 and 1.5 degrees Celsius individually.

Figure 5 provides a visual representation of mean temperature predictions for both the world (a) and China (b) as an indicator of their progress toward SDG 13, which focuses on climate action. The data source for this analysis is NOAA, which records mean global temperatures from various locations worldwide, with values converted from absolute temperature measurements to temperature anomalies. On the left side of the analysis, it is evident that global temperatures have been on the rise since 1850, with a peak in the 2000s. Notably, 2021 marked the hottest year on record. As documented by Hagedorn et al. [53], global mean temperatures have increased by 1 degree Celsius, half of which has occurred in the past three decades. This analysis supports this trend, as we were nearing the 1-degree Celsius mark in 2021. Our forecast predicts that by 2030, global temperatures will decrease by approximately 0.53 degrees Celsius, representing a 36% reduction. This projection suggests that the target of limiting global warming to 1.5 degrees Celsius is achievable. It is worth noting that while some studies, such as Onu [23], indicate continued global temperature increases, our forecast suggests a decline in global temperatures in 2030. This projection hinges on countries fulfilling their NDCs and intensifying their climate goals, especially during the implementation phase of the Accord. Our analysis emphasizes that achieving the 1.5 and 2 degrees Celsius targets by 2030 is feasible if the world diverges from business-as-usual (BAU) practices and adopts more ambitious measures. On the right side of the analysis, there is a significant estimation for China. In 2021, China’s mean temperature stood at 8.19 degrees Celsius and is projected to decrease to approximately 7.2 degrees Celsius by 2030, marking a 12% decline. This aligns with a study by [18], which underscores that China’s temperature projection remains controlled and is expected to surpass the global average, as confirmed by Biro et al. [54]. This forecast reinforces the notion that China is on a trajectory to achieve its Paris Agreement targets of 1.5 and 2 degrees Celsius [28,51,55].

5. Conclusions

In this paper, we analyzed the progress of China vis-à-vis the world in achieving the PA of limiting the rise in the global temperature to between 1.5 and 2 degrees Celsius by 2030. In this regard, we draw a comparison between China and the rest of the world. Data were derived from the WDI for the period 1990–2021. The annual mean temperature data were obtained from the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. Taken together, these findings suggest that China is well on track to achieving its PA of limiting the increase in the global temperature compared to before industrial levels to 1.5 degrees Celsius levels.

Sustainable Development Goals 7 and 13 continue to garner substantial global and Chinese support, as they play a crucial role in pushing the boundaries toward achieving the PA. However, the global community still faces challenges in attaining the targets set forth in SDGs 7 and 13. The analysis underscores China’s significant progress toward fulfilling its commitments under the PA, while the rest of the world lags behind. To bridge this gap and make substantial advancements in SDGs 7 and 13, nations must consider implementing drastic changes in their national strategies. Furthermore, there is a pressing need for a global reduction in energy intensity, given its notably high levels worldwide. In this context, China stands out as a nation that has consistently and successfully reduced its energy intensity levels over time. Moreover, solar and wind energy sources remain the fastest-growing renewable energy options, both globally and within China. Notably, China has emerged as a world leader in the development and deployment of renewable energy technologies. Achieving these admirable goals necessitates a more robust policy framework that supports the integration of renewable energy, pursues full electrification, and prioritizes the crucial process of decarbonization.