1. Introduction
Several crises, such as water scarcity, food insecurity, and public health emergencies, are being brought on by recent trends in global warming, which have led to a persistent degradation of the ecological environment and a threat to human survival [
1,
2,
3]. The primary cause of global warming is the increase in carbon dioxide emissions. As a result, the world community has focused its efforts on reducing these emissions, encouraging low-carbon, sustainable growth and supporting ecological preservation [
4,
5,
6]. Following a sharp increase in emissions, China overtook the US as the world’s top carbon producer in 2007 [
7]. By 2022, China’s carbon emissions had surged to 11.48 billion tons, marking an annual growth rate of 3.7% since 2018 [
8]. Predominantly, these emissions stem from fossil fuels, comprising 84.1% of China’s total in 2020 [
9]. As a result, China has actively looked for ways to reduce its carbon footprint, set several goals and policies [
10,
11], and, at the 75th UN General Assembly, declared its intention to achieve carbon neutrality by 2060 and a carbon peak by 2030, reiterating its commitment to a comprehensive green transition [
12].
Counties, serving as the core administrative entities and spatial foundations for industrial and economic expansion in China [
13], encompass 78% of the nation’s landmass, are home to two-thirds of its population, and contribute approximately 51.8% to China’s GDP [
14]. The county-level administrative divisions here include counties (such as Deqing county, Cangnan county, and Panan county), county-level cities (Dongyang City, Cixi City, and Yueqing City), and districts (Wucheng district, Yuhang district, and Haishu district) under city jurisdiction. Notably, they are responsible for over half of the nation’s carbon emissions [
15], underscoring the immense potential and urgent need for low-carbon initiatives at this level, which are crucial for China’s carbon neutrality aspirations [
16]. Achieving these reduction goals entails national macro-strategic planning and an emphasis on localized carbon emission strategies [
17]. Hence, it is crucial to examine county-level carbon emissions thoroughly, accurately chart their spatio-temporal progression, and comprehend the factors that drive them. Acquiring this knowledge is essential for fostering regional cooperation in sustainable growth and improving the scientific accuracy, execution, and efficacy of energy conservation and emissions reduction strategies [
15].
Researchers in the modern era have used a wide variety of analytical tools to focus more on the temporal and spatial patterns of carbon emissions. Duman [
18] utilized the standard deviational ellipse (SDE). They observed a progressive change in the trajectory of urban carbon emissions in China, transitioning from a “northeast–southwest” to a “northwest–southeast” orientation. Concurrently, the standard deviation ellipse grew, indicating that air pollution and carbon dioxide emissions had expanded in space over time. Liu [
19] utilized spatial autocorrelation (SA) to reveal notable geographical variation, self-correlation, and spillover effects associated with urbanization and carbon emissions. Zhang [
20] utilized Kernel Density Estimation (KDE) for their study, effectively pinpointing spatial associations between carbon emission patterns and land-use-related emissions. In another investigation, Soares [
21] utilized the Gini Index (GI) to analyze the world’s 50 major economies and discovered a strong correlation between economic levels and carbon emissions, revealing that higher economic levels lead to greater carbon emissions. Williams [
22] employed the Coefficient of Variation (CV) to analyze carbon emission patterns in the Southern Ocean and discovered that this region significantly contributes to the sequestration of heat and the absorption of anthropogenic carbon. These research works provide essential perspectives on the spatio-temporal patterns of carbon emissions. However, the main focus of these studies has been on the provincial and municipal levels, with significantly less attention paid to the county level. This discrepancy underscores the necessity for more detailed research at a finer scale. Delving into these aspects at a more granular level is crucial for understanding the subtle dynamics of spatial interactions and the specific impacts between neighboring areas. Such an approach can aid in the formulation of more effective and targeted decision-making and planning strategies.
Understanding the factors influencing carbon emissions is vital for advancing carbon reduction and environmental improvement. Scholars frequently employ methods like Spatial Econometric Regression Models (SEMs), Logarithmic Mean Divisia Index (LMDI), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR) models, and STIRPAT models in their research. For example, Qu [
23] analyzed the spatial response mechanism between carbon emission efficiency and ecosystem services using SEMs, finding that overall ecosystem services are influenced not only by local carbon emission efficiency but also by that of surrounding areas. Quan [
24] and González [
25], through their analysis with the LMDI model, discovered that in Spain, factors related to per capita output are predominant in the industrial sector, while factors related to carbonization effects play a critical role in the commercial sector. Khodakarami [
26] used the GWR model to study how trees capture carbon, absorb CO
2, and produce oxygen across different areas and what affects these processes. He found that the carbon-absorbing services of urban green spaces are key to cutting greenhouse gas emissions and supporting sustainable environmental development. Li [
27] utilized the MGWR model to investigate the determinants of urban carbon emissions in China. Their findings uncovered geographical variations in the effects of per capita GDP, secondary industry percentage, and population density on carbon emissions. Nosheen [
28] used the STIRPAT model to analyze the factors affecting carbon emissions in some Asian countries and found that energy consumption and urbanization contribute to increasing carbon emissions. Although these studies provide valuable insights into the factors affecting carbon emissions, they often focus more on the overall positive and negative impacts during the entire phase, without delving into the temporal and spatial changes of these factors. Thus, there is a need for in-depth research from different temporal and spatial perspectives to clearly observe the changes in the time series of influencing factors and to reveal local factors causing regional disparities. By expanding the research scope to these aspects, it becomes possible to gain a more detailed understanding of the reasons affecting carbon emissions, thereby formulating more targeted and effective mitigation and adaptation strategies.
This article endeavors to clarify the spatial–temporal aspects of carbon emissions at the county scale in Zhejiang Province using ESTDA methods, including LISA time path and LISA spatio-temporal transition, as well as the standard deviational ellipse. The GTWR model is utilized to investigate the factors influencing carbon emissions at this level. This study is innovative in two key respects: Firstly, it adopts a micro-scale approach, analyzing the spatio-temporal attributes of carbon emissions at the county level in Zhejiang Province, marking a shift from static to dynamic research methodologies. Secondly, it leverages the GTWR model’s capacity to account for the spatio-temporal heterogeneity of influencing factors. It enables a more nuanced understanding of these factors on carbon emissions, in contrast to traditional global regression methods. This approach is valuable for policymakers developing targeted carbon reduction strategies. The research objectives were as follows: (1) to collect and examine Zhejiang Province county-level carbon emission data in order to shed light on the spatio-temporal aspects of these emissions; (2) to create and employ a GTWR model for analyzing the factors that affect carbon emissions in different spatial and temporal dimensions within Zhejiang Province; and (3) to integrate the findings from both analyses to formulate viable strategies and recommendations for reducing carbon emissions at the county level.
The remainder of this work is organized in the following manner:
Section 2 outlines the data sources and methodologies used in this research.
Section 3 explores the spatial and temporal dynamics of carbon emissions, along with their determinants at the county level in Zhejiang Province.
Section 4 consolidates the key results, discusses possible approaches and suggestions, recognizes the limitations of this study, and suggests future research avenues and areas of focus.
Based on this research gap, this study aimed to investigate the spatio-temporal patterns of carbon emissions and their influencing factors from a micro-perspective of county scale. Therefore, we proposed two research questions:
RQ1. How do the local development directions and global trajectories of the spatio-temporal pattern of carbon emissions at the county level in Zhejiang Province change?
RQ2. What factors drive the changes in the spatio-temporal pattern of carbon emissions at the county level in Zhejiang Province?
This paper contributes to the literature on carbon emissions in several ways: First, it delves into the county scale, moving beyond the provincial and city scales. Second, it examines the spatio-temporal pattern of carbon emissions at the county level in Zhejiang Province from both local and global dimensions. Third, it explores the factors affecting the spatio-temporal distribution of carbon emissions at the county level in Zhejiang Province and analyzes the spatio-temporal changes of significant influencing factors.
4. Conclusions
This study examined the spatial and temporal patterns of carbon emissions in the county areas of Zhejiang Province, considering global climate warming and China’s goals of achieving carbon peak and carbon neutrality. Utilizing the GTWR model, it examined the factors influencing carbon emissions in these county areas. The conclusions are as follows:
(1) LISA time path analysis results indicated that, overall, the county-level carbon emissions in Zhejiang Province from 2002 to 2022 exhibited strong local spatial structure stability, with an upward trend in this stability. Spatially, the most robust regional spatial structural stability of carbon emissions was found in western Wenzhou and Lishui in southern Zhejiang, Quzhou in Zhejiang’s west, and western Taizhou and western Hangzhou, showing a trend of spreading from south and west Zhejiang to northern and eastern Zhejiang. The spatial pattern of carbon emissions in the county areas of Zhejiang Province possesses specific spatial integrative characteristics. However, this integrative tendency is generally weakening, with non-synergistic growth predominantly exhibiting a fragmented pattern.
(2) The spatio-temporal transition results from the Moran’s scatterplot indicated that the most common transition was Type IV, with solid stability in the Moran’s scatterplot. This suggests a relatively high spatial accumulation of carbon emissions in the county areas of Zhejiang Province, a high degree of path dependence in carbon emissions spatial aggregation, and relative stability in the spatial structure of carbon emissions. This reflects that spillover effects from surrounding regions influence local county areas less, and internal factors significantly impact changes in their carbon emission spatial structure.
(3) A consistent “northeast–southwest” pattern emerged in the spatial distribution as the axis length varied. From 2002 to 2022, the centroid of county-level carbon emissions in Zhejiang Province varied within the range of 120.551° to 120.570° E and 29.555° to 29.591° N, moving generally northeastward and overall forming a ‘V’ shape. This indicated that the rate of increase in carbon emissions in the northeastern counties of Zhejiang was higher than the average, with the areas of high growth in overall carbon emissions still developing towards the northeast direction.
(4) The elements that influence carbon emissions vary significantly in terms of space and time. These factors, ranked in descending order of their degree of influence are population size, urbanization rate, industrial structure, and economic development level. Among these, population size, urbanization rate, and economic development level mainly promote carbon emissions, while industrial structure acts to suppress emissions. While Zhu [
29] argued that the growth of the secondary industry has significantly worsened carbon emissions, our research shows that the secondary sector reduces carbon emissions. This finding aligns with the studies of Qi [
17] and Yang [
60].