Spatial and Temporal Differentiation of Carbon Emission Efficiency and the Impact of Green Technology Innovation in Hubei Province

Abstract

Green technological innovation is pivotal in advancing the ‘dual carbon’ target and promoting sustainable and low-carbon development. This research examines 17 prefecture-level cities in Hubei Province, employing the Super-SBM model for assessing emissions of carbon efficiency from 2010 to 2020. The kernel density estimation and the Dagum coefficient of Gini are also used to examine the spatio-temporal differentiation and the evolution of these efficiencies. A data panel regression model is utilized to evaluate how green technological innovation impacts carbon emission efficiency in Hubei Province. The research revealed that (1) Hubei Province’s carbon emission efficiency first fluctuated and then increased rapidly, and (2) the overall regional difference in carbon emission efficiency in Hubei Province shows a trend of first decreasing and then gradually increasing. The Wuhan metropolitan area and the Xiang-yang-Shiyan-Suizhou-Shennongjia urban area are quite different; the differentiation within the Yichang-Jingzhou-Jing-Enshi urban agglomeration shows a narrowing trend. (3) The innovation elements of green technology are positively correlated with the effectiveness of carbon emissions; the relationship between economic expansion and population density among the control variables also shows a positive correlation, while the industrial structure and government environmental regulations are negatively correlated. (4) In Hubei Province, there is a temporal lag between green technological innovation and its impact on carbon emission efficiency. Capital investment and technical achievement currently enhance carbon emission efficiency, while human capital positively affects carbon emission efficiency during a second lag period. This article proposes countermeasures and recommendations for R&D capital spending, innovative talent cultivation, and regional differentiation, providing specific references to advance the coordinated growth of the whole Hubei Province and green sustainable development.

1. Introduction

Since the Industrial Revolution, greenhouse gas emissions have caused global warming to intensify, triggering extreme weather and natural disasters that have seriously hindered the progress of human civilization. In response, nations have established ‘dual carbon’ objectives to advance energy efficiency and conservation and reduce emissions. To combat climate change and seek a sustainable development model of harmonious coexistence between humans and nature, Xi Jinping announced at the 75th General Assembly of the United Nations that China is committed to taking more rigorous measures, striving to peak carbon dioxide emissions by 2030 and attain carbon neutrality by 2060 [1]. Green technology is a series of emerging technological methods developed to reduce resource consumption, lower environmental pollution, rehabilitate the ecological environment, advance the development of sustainable civilization, and attain a peaceful relationship between humanity and nature. Carbon emission efficiency is a metric for comprehensive efficiency that measures the economic and social benefits of carbon emissions from economic production activities by combining the gross domestic product with carbon dioxide emissions. Innovation is a key engine for sustainable economic and social development. The comprehensive execution of the innovation-driven growth strategy is essential for improving carbon emission efficiency and attaining sustainable development. Hubei Province is a crucial geopolitical pivot for advancing the elevation in the central region, leading to a change in economic growth. As one of the country’s seven principal carbon trading pilot provinces, its success in green innovation holds significant implications for national decreases in carbon emissions.

Mitigating carbon emissions and enhancing carbon emission efficiency have emerged as essential breakthroughs in the economy’s and ecology’s sustainable development. In light of the significant focus on domestic and international low-carbon development objectives, pertinent scholars have undertaken comprehensive investigations into the efficiency of carbon emissions. The study has concentrated on three facets: (1) Constructing evaluation indicators for emissions of carbon efficiency. The measurement of singular variable indicators cannot reflect the systematic characteristics of carbon emissions as an internal factor of the economy and society and has limitations [2,3]. In light of this, Ramanathan presents the viewpoint of total variables, encompassing the comprehensive roles of capital, energy consumption, and labor, all thoroughly evaluated [4]. The multi-factor index measurement method comprises metric and not parametric methodologies [5]. Among them, the stochastic frontier model in the parameter method measures the efficiency of single outputs and multiple inputs. Non-parametric methods currently use models such as SBM-Undesirable and Super-SBM for measurement, which have advantages in studying the efficiency of various inputs and outputs [6,7]. (2) The study focuses on the spatio-temporal differentiation of carbon emission efficiency. Researchers often use methods like the coefficient of Gini and the estimation of kernel density to examine regional disparities and geographical correlations in carbon emission efficiency. The Gini coefficient can comprehensively consider the differences between and within regions, but missing or inaccurate data may affect the reliability of the results [8]. The kernel density estimate can be flexibly adapted to various data distributions, but probability leakage may occur when dealing with nonnegative data [9]. Relevant studies have suggested that China’s carbon efficiency emissions are gradually improving, with significant spatial agglomeration, spillover effects, and significant regional differences [10,11]. (3) Factors affecting carbon emissions, methods such as the index decomposition model (LMDI) [12], STIRPAT model [13], and decoupling analysis [14] are often used to study the impact of economic development, national policies, and green technological innovation on carbon emission efficiency. Du et al. (2019) discovered that green technology innovation improves carbon emission efficiency by fostering technological advancement [15]. Li et al. (2020) examined the determinants affecting energy emission reductions in China’s eight principal economic zones [16]. They assert that technical advancement enhances the effectiveness of carbon emissions [16]. Conversely, scholars like Yang et al. (2023) have claimed that innovative green technological advancements positively influence carbon emission efficiency through resilience effects [17]. Zhou et al. (2024) discovered that green technological innovation influences the reduction of carbon emissions via a ’resilience effect’ and a ’polarization-trickle-down’ impact based on an analysis of 110 cities in the Yangtze River Economic Belt [18].

At present, green technological innovation has become the focus of research for many scholars, who usually analyze it from the dimensions of R&D investment, evaluate technological output, analyze the effectiveness of green technological advances, and establish a comprehensive index method to measure the extent of green innovation in technology. For instance, Fang (2023) utilize the number of green patents to assess the innovative green technology capability of Chinese cities [19]; Wang et al. (2022) use the SBM model to evaluate the effectiveness of green innovation [20]. Current research has concentrated on traditional technological developments in mitigating carbon emissions and contributing green technological innovation to regional sustainability. In contrast, the influence of green technology innovation on the mitigation of carbon emissions has been subject to comparatively limited research. Findings from studies indicate that the effects of green technology innovation are both incremental and intricate. Li et al. (2022) [21] showed in their study that green technological innovation positively influences the region’s green development. Still, the effect on adjacent areas is insignificant [21].

Research across several geographical contexts has demonstrated that China’s low-carbon urban pilot program initiatives have markedly influenced urban carbon emissions and the efficacy of collaborative governance [22]. Research of 19 OECD nations revealed that the discovery and implementation of ecologically linked technologies is a crucial component in enhancing carbon emission efficiency [23]. Specific research indicates that government action alone may exert an inhibitory influence on carbon emission efficiency across various industrial scenarios. The interaction effect of green finance substantially influences carbon emission efficiency [24]. Some studies, taking China’s high-energy-consuming enterprises as an example, develop green financial technologies that can enhance the efficiency of green bond issues and offer financial assistance for renewable energy initiatives [25].

Divergent perspectives remain regarding the impact of green technology innovation on the effectiveness of carbon emissions, and this topic is somewhat controversial. Moreover, prior research has concentrated mainly on the national, river basin, and urban levels [26,27], as well as industries such as transportation [28], construction [29], agriculture [30], and manufacturing [31]. Qian et al. (2015) [32] utilized the Malmquist–Luenberger score methodology to assess the effectiveness of carbon emissions in East Asia from 1995 to 2012. The general trend in carbon emission efficiency is declining, with significant disparities among economies and an increasing divergence [32]. Yuan et al. (2017) [33] employed the highly efficient SBM model to determine how much carbon China’s provincial transportation emits. There is an apparent spatial clustering of changes in carbon emission efficiency [33]. Nonetheless, studies on the spatio-temporal shift in carbon emission efficiency in particular provinces or counties are scarce. We need to conduct further in-depth analysis based on local conditions and formulate more targeted strategies.

This paper uses Hubei Province as its research area and the Super-SBM model to determine how efficient carbon emissions are. It employs kernel density estimation and the Dagum coefficient of Gini to examine the distinctions between space and time. The regression model for the panel dataset and the dynamical panel extended method of moments are employed to investigate the impact of green technology innovation on the production of carbon dioxide emissions in Hubei Province, thereby establishing a foundation for the province’s transition to a sustainable and carbon-conscious economy and facilitating coordinated development, as well as offering scientific support for advancing sustainable regional development.

2. Materials and Methods

2.1. Study Area

Located in central China, Hubei Province lies in the middle reaches of the Yangtze River system (Figure 1). Its geographical location is the range of 29°01′53″ to 33°06′47″ N and the longitudinal range of 108°21′42″ to 116°07′50″ E, covering an administrative area of 185,900 square kilometers. It has long been known as the ‘thoroughfare to nine provinces’ and has a unique geographical advantage. Hubei, which has the longest coastline on the Yangtze River, holds a significant strategic position within the Yangtze River Economic Belt and is actively advancing the ‘one central body, two wings’ regional development pattern to enhance the regional economy’s superior advancement. According to the regional growth layout proposed in Hubei Province’s 14th Five-Year Plan, Hubei Province is divided into three major regions: the Wuhan Metropolitan Region, which includes Wuhan, Huangshi, Ezhou, Xiaogan, Huanggang, Xianning, Xiantao, Tianmen, and Qianjiang; and ’Xiangyang-Shiyan-Suizhou-Shennongjia’ urban agglomeration, which provides for Xiangyang, Shiyan, Suizhou, and Shennongjia Forest District; and the ‘Yichang-Jingzhou- Jingmen-Enshi’ urban agglomeration, which includes Yichang, Jingzhou, Jingmen, and Enshi Tujia and Miao Autonomous Prefecture. The core areas of the three major urban agglomerations are the Wuhan metropolitan area with Wuhan, Ezhou, Huangshi, and Huanggang as its core; the Xiangyang metropolitan area with Xiangyang as its core; and the ‘Yichang-Jingzhou-Jingmen’ metropolitan area, excluding Enshi Autonomous Prefecture. The three urban agglomerations are connected by the ‘Hanshi’ development zone, the riverside development zone, and the ‘Xiangyang-Jingmen-Yichang’ development zone to promote comprehensive regional development. High-quality economic development is inseparable from ecological civilization, and green and sustainable development needs to be led by innovation to achieve regional integration.

2.2. Data Sources

The research employs a panel encompassing 17 prefecture-level cities in Hubei Province over the 2010–2020 period for empirical analysis. The emissions of carbon data are sourced via the China Carbon Accounting Database, which has constructed a carbon emissions inventory for 287 Chinese cities. The inventory encompasses 47 socio-economic sectors and incorporates carbon emissions from 17 energy-related petroleum, coal, and cement manufacturing categories. The emissions inventory is derived from the city’s energy usage sheet. The inventory’s scope and form correspond with China’s national and provincial inventory [34,35,36,37]; data on R&D expenditure as a share of GDP, R&D personnel, GDP per person, population density, and the proportion of the secondary industry in Gross Domestic Product (GDP), industrial wastewater, industrial sulfur dioxide, and particulate matter and smoke were obtained from the statistics yearbooks of Hubei Province and its many prefecture-level cities. We received the number of granted utility models and patents for inventions through the Patent Search and Analysis System of the China National Intellectual Property Administration.

2.3. Research Methods

2.3.1. Super-Efficient SBM Model

The concept of the data envelope analysis was introduced by Chames in 1978 to assess the comparative efficiency of the units that make decisions in a ’many input and multiple output’ framework [38]. However, the errors caused by slack variables are easily ignored. Tone suggested a non-radial and non-angular SBM model [39]. The SBM model makes it challenging to rank efficiency and is prone to decision-making bias. The super-efficiency model can handle such units and achieve comparisons between decision-making units [40]. This paper establishes an input–output index framework table to assess the efficiency of carbon emissions in Hubei Province (

Table 1. Input–output indicator system for carbon emission efficiency in Hubei Province.

Variable Name	Specific Indicator	Description of the Indicator
Input indicators	Capital factor	Gross fixed asset investment [45]
	Labor factor	Number of employees [10]
	Energy factor	Total annual electricity consumption [42]
Output indicators	Desired output	Regional gross domestic product [46]
	Undesired output	Carbon dioxide emission [47]

) utilizing the Cobb–Douglas function of production [41]. The total fixed asset investment, staff count, and yearly power usage are input indicators derived from the three dimensions of capital, energy, and labor [42]. Among the expected outputs, the regional GDP is used to represent economic benefits [43]. The unanticipated production indicator is denoted through carbon dioxide emissions [44]. MATLAB R2022b software is used to compute the efficiency of emissions of carbon value for Hubei Province using the super-efficient SBM model. The expression is:

$$\mathrm{Min}\rho ={\displaystyle \frac{\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{\overline{x_i}}{x_{i0}}}}{\frac{1}{s_1+s_2}\left({\displaystyle \sum _{q=1}^{S_1}\frac{{\overline{y}}_q^W}{y_{q0}^W}}+{\displaystyle \sum _{q=1}^{s_2}\frac{{\overline{y}}_q^b}{y_{q0}^b}}\right)}}$$

(1)

Among them: $\overline{\mathrm{x}}\ge {\displaystyle \sum _{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\theta }_{\mathrm{j}}}{\mathrm{x}}_{\mathrm{j}}$; ${\overline{\mathrm{y}}}^{\mathrm{w}}\le {\displaystyle \sum _{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\theta }_{\mathrm{j}}}{\mathrm{y}}_{\mathrm{j}}^{\mathrm{w}}$; ${\overline{\mathrm{y}}}^{\mathrm{b}}\ge {\displaystyle \sum _{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\theta }_{\mathrm{j}}}{\mathrm{y}}_{\mathrm{j}}^{\mathrm{b}}$; $\overline{x}\ge x_0$; $0\le {\overline{y}}^w\le y_0^w$; ${\overline{y}}^b\ge y_0^b$; $\theta \ge 0$.

In the above formula: $\rho$ is the efficiency value, $m$, $n$, $S_1$, $S_2$ are the number of input indicators and decision units, decision units, anticipated outputs, and unanticipated outputs in turn; ${\chi }_{io}$, $y_{q0}^w$, $y_{q0}^b$ are the input indicators, expected outputs, and non-expected output indicators, respectively; and ${\overline{x}}_i$, ${\overline{y}}_q^w$, ${\overline{y}}_q^b$ represent the unmet variables for input from the user, anticipated output, and non-anticipated outcome.

2.3.2. Panel Data Regression Model

This research develops a panel data regression model to eliminate the dimension; the variable data are processed pairwise. Utilizing the panel data from Hubei Province from 2010 to 2020, the relationship between carbon emission efficiency and the influencing factors of green technological innovation can be defined as the following panel regression model:

$${\mathrm{lnCV}}_{it}={\mu }_0+{\mu }_1\ln F{I}_{it}+{\mu }_2\ln H{C}_{it}+{\mu }_3\ln T{A}_{it}+{\mu }_4\ln E{D}_{it}+{\mu }_5\ln I{S}_{it}+{\mu }_6\ln P{D}_{it}+{\mu }_7\ln G{R}_{it}+{\gamma }_i+{\phi }_t+{\epsilon }_{it}$$

(2)

$i$, $t$ are city and year, respectively, $CV$, $FI$, $HC$, $TA$, $ED$, $IS$, $PD$, $GR$ represent carbon emission efficiency, investment in research and development, human capital, technological achievements, economic development level, industrial structure, population density, and government environmental regulations, in that order. ${\mu }_0$ for the constant term, ${\mu }_1$, ${\mu }_2$, ${\mu }_3$, ${\mu }_4$, ${\mu }_5$, ${\mu }_6$, ${\mu }_7$ are the respective elastic coefficients; ${\gamma }_i$ denotes the fixed municipal variable, ${\phi }_t$ represents the fixed time variable, and ${\epsilon }_{it}$ denotes the random disturbance term.

2.3.3. Dynamic Panel Generalized Method of Moments (GMM)

Dynamic GMM estimation, also known as generalized moment estimation, estimates parameters under the condition that the actual parameters satisfy specific moments and is widely used in economic research. Dynamic GMM estimation is a further development of panel data models, which incorporate one or more lags of the explained variable in the explanatory variables. A dynamic GMM model combines the effects of capital investment, human capital, and technological progress on carbon emission efficiency over time. This is shown in Equation (3):

$$\ln C{V}_{it}={\alpha }_i+{\theta }_t+{\displaystyle \sum _{l=0}^3{\vartheta }_l}\ln C{V}_{it-l}+{\displaystyle \sum _{l=0}^3{\beta }_l}\ln G{I}_{it-l}+{\delta }_1\ln E{D}_{it}+{\delta }_2\ln I{S}_{it}+{\delta }_3\ln P{D}_{it}+{\delta }_4\ln G{R}_{it}+{\epsilon }_{it}$$

(3)

$i$, $t$ represent the city and year, ${\alpha }_1$ expresses a constant (math.). The constant represents the time effect, and l represents the lag order, which can take on values of 0, 1, 2, or 3. ${\vartheta }_l$, ${\beta }_l$, $\delta$ are corresponding regression coefficients for each variable, ${\epsilon }_{it}$ are random errors.

3. Results and Analysis

3.1. A Study of the Temporal and Spatial Differentiation of Carbon Emission Efficiency in Hubei Province

3.1.1. Time Series Analysis of Carbon Emission Efficiency in Hubei Province

The Super-SBM model assessed the carbon emission effectiveness of 17 prefecture-level cities in Hubei Province from 2010 to 2020. Figure 2 shows the evolution trend of the time series. From 2010 to 2015, there was fluctuating growth, with the carbon emission effectiveness rising from 0.36 in 2010 to 0.39 in 2015. The Provincial Development and Reform Commission developed the Hubei Province Low-Carbon Development Plan (2011–2015) to address climate change proactively, decisively exploring low-carbon development pathways, and initial success has been achieved in reducing greenhouse gas emissions; 2015–2020 saw a rapid recovery, with Hubei Province’s carbon emission efficiency rising from 0.39 in 2015 to 0.54 in 2020. Hubei is one of the seven experimental provinces and municipalities that trade carbon emissions, actively exploring new experiences in the carbon market and sustainable development to enhance carbon emission efficiency. It has launched the ‘Blue Sky Defence War’ action plan to regulate scattered and polluting enterprises and strengthen the coordinated control of air pollution.

From the viewpoint of the three principal urban agglomerations, the overall carbon emission efficiency exhibited initial fluctuations followed by increased growth in the subsequent phase. The Wuhan Metropolitan Area and the ‘Yichang-Jingzhou-Jingmen-Enshi’ urban agglomeration experienced large fluctuations in the increase in carbon emission efficiency from 2010 to 2015 and achieved rapid growth from 2015 to 2020. The ‘Xiangyang-Shiyan-Suizhou-Shennongjia’ urban agglomeration underwent relatively stable transformations in the initial phase, followed by growth in the subsequent phase. The Wuhan Metropolitan Region is the main engine of high-quality development in Hubei Province. The ’Yi Jing Jing’ golden triangle and Enshi will build an excellent socio-economic development belt focusing on the green economy and strategic emerging industries, with rapid growth in carbon emission efficiency. The ‘Xiang Shi Sui Shen’ urban agglomeration will vigorously develop ecological agriculture and jointly build a green organic agricultural product industry chain with overall stable advancement in carbon emission efficacy.

Figure 3 illustrates the kernel density emissions from carbon efficiency across 17 prefecture-level cities in the province of Hubei. Based on the location near the apex, there was a brief trend of the central peak moving to the left in the early stage and then moving to the right in the later stage, indicating a short decline in carbon emission efficiency at the beginning and a gradual increase in the last stage. In terms of shape, the central peak in the early period changed from ’tall and thin’ to ’short and fat’ in the later period, indicating that the disparity in carbon emissions efficiency in Hubei Province was comparatively minor in the initial era and progressively augmented in the subsequent period. There were side peaks in 2012 and 2020, indicating that in a few years, the regional difference in carbon emission efficiency in Hubei Province experienced polarization, but overall, it is relatively stable. Regarding distribution continuity, Hubei Province’s carbon emission efficiency is relatively concentrated each year, and there is no apparent trailing phenomenon. This means that its carbon emission efficiency has not exhibited significant polarization.

3.1.2. Spatial Differentiation of Carbon Emission Efficiency in Hubei Province

This research employs Dagum’s deconstruction method of dividing the Gini coefficient by subgroups for assessing the extent of regional differentiation in the variation of carbon emission efficiency in Hubei Province [48]. This method can effectively solve the source of spatial differentiation and the overlap problem between sub-samples [48,49]. Figure 4 depicts the carbon efficiency emissions fluctuations and the Dagum Gini coefficient in Hubei Province between 2010 and 2020. The spatial differentiation in carbon efficiency emissions in Hubei Province showed a general increase between 2010 and 2019. The average annual mean growth rate of the Gini coefficient for emissions of carbon efficiency in Hubei Province throughout the examination period was 5.26%, with the Gini coefficient rising to the highest in 2019. The Wuhan metropolitan area ranked second, and the ‘Xiangshi Suishen’ urban agglomeration had the slightest degree of differentiation, exhibiting an average Gini value of 0.14. Concerning the evolving trend, the Gini coefficients of the Wuhan metropolitan area and the ‘Xiangshi Suishen’ urban agglomeration showed a significant upward fluctuation. The Yijiang-Jingmen-E’zhou urban agglomeration has a slightly smaller range of Gini coefficient fluctuations, exhibiting an average annual decline of 2.79%. The data above indicate substantial regional disparities in carbon emission efficiency within Hubei Province, with increasing divergence across the province, the Wuhan metropolitan area, and the ‘Xiangshi Suishen’ urban agglomeration. The differentiation within the Yichang-Jingmen-Jingzhou-Jingmen-Ezhou urban agglomeration is gradually decreasing.

Figure 5 depicts the degree of regional differentiation in the efficiency of carbon emission in Hubei Province and the sources of these disparities. The broken line shows the variation in the Dagum Gini coefficient among regions. The results show that during the entire period of investigation, the degree of differentiation between the ‘Wuhan City Circle’ and ‘Yi Jing Jing En’ urban agglomerations was the greatest, followed by the ‘Xiangshi Suishen’ urban agglomeration and the ‘Yi Jing Jing En’ urban agglomeration, and between the ‘Wuhan City Circle’ and ‘Xiangshi Suishen’ urban agglomerations. Regarding trends, the spatial differentiation between most regions has tended to increase. The Gini coefficient of carbon emission efficiency among urban agglomerations has decreased slightly since 2019. The pandemic outbreak at the end of 2019 caused production to stagnate in various regions, leading to a decrease in the variance in carbon emissions among regions.

The column chart illustrates the variation in the sources of the Dagum Gini coefficient disparity regarding carbon emissions within areas in Hubei Province. The super-variability density contribution rate to the overall geographical differentiation in Hubei Province is the highest, followed by the intra-regional and inter-regional differentiation rates for contributions. The above results show that the predominant contribution to the spatial differentiation in carbon emission efficiency is super-variability density; the extent of overlap in carbon emission efficiency among different regions urgently requires corresponding attention.

The study selected cross-sectional data from four time periods: 2010, 2013, 2016, and 2020. The natural breakpoint approach was utilized to categorize the carbon emission efficiency of Hubei Province into five levels, which illustrated the geographical distribution of carbon efficiency across prefecture-level cities (Figure 6). Hubei Province has apparent spatial differentiation in the efficiency of carbon emissions. Between 2010 and 2020, the number of high-value carbon emission efficiency regions in prefecture-level cities in Hubei Province steadily rose. Wuhan exhibits the highest carbon emission efficiency value in the Wuhan metropolitan area, decreasing progressively toward the surrounding prefecture-level cities. The surrounding cities of Wuhan have taken over industrial transfers, mainly resource-intensive industries, leading to diminished carbon emission efficiency. From 2010 to 2020, the number of high-value regions with high carbon emission efficiency in the Wuhan metropolitan area gradually rose to four. With the integrated development of the Wuhan metro area, industrial linkages have driven rapid improvements in carbon emission effectiveness in adjacent cities. Ezhou City, which is adjacent to Wuhan, has maintained a relatively low level of carbon emission efficiency from 2010 to 2020. Ezhou City has a small administrative area, and its fixed asset investment, number of employees per unit, and GDP are all lower than those of Huangshi and Huanggang. Although it is adjacent to Wuhan City and is actively involved in constructing Wuhan New City and transferring corresponding industries, the industrial framework is primarily characterized by the secondary sector. At the same time, the enhancement of energy use efficiency progresses at a very sluggish pace. Moreover, Ezhou City’s urbanization rate ranks second in Hubei Province, and the urbanization process has accelerated significantly. This means that even with an increase in fixed asset investment, its carbon emission intensity remains relatively high, and it is challenging to enhance carbon emission efficiency.

Within the metropolitan area of ‘Xiangshi Suishen’, the average carbon emission efficiency of Xiangyang and Suizhou from 2010 to 2020 was 0.50 and 0.52, respectively, which has always been relatively high. As one of the ‘two wings’, Xiangyang has seen the development and growth of emerging technology industries, and its carbon emission efficiency per unit of economic development is relatively high. Following the 13th Five-Year Plan, Suizhou City has strengthened the management of total energy consumption and dual control targets, advocated for eco-friendly and low-carbon transformation within the industrial framework, and continuously improved the city’s energy management and service levels conservation. The carbon emission efficiency of Shiyan City varied from 0.37 in 2010 to 0.42 in 2020, with an average value of 0.36, which is at the medium level of the third tier of carbon emission efficiency. The automobile sector in Shiyan is gradually relocating, prompting an active adjustment of the economic framework and establishing a green and low-carbon demonstration zone to sustain carbon emission efficiency at an intermediate level. The efficiency of carbon emissions in the Shennongjia Forest District increased from 0.11 in 2010 to 0.26 in 2020, with an average value of 0.16. Shennongjia Forest District mainly focuses on nature conservation and the development of ecotourism. Its carbon emissions are below the provincial average, but the magnitude of economic development is relatively small. As road infrastructure improves and the tourism sector develops further, the carbon emissions in terms of economic output are comparatively elevated. Consequently, the overall effectiveness of carbon emissions is marginally reduced.

In the ‘Yichang-Jingzhou-Jingmen-Enshi’ urban agglomeration, each city’s carbon emission efficiency has increased yearly. With ecological protection in mind, the four towns signed the Yi Jing Jing’en Urban Agglomeration Ecological Environment Cooperation Agreement to establish a joint prevention and control mechanism for the ecological environment and air pollution, significantly improving the emission of carbon efficiency. The carbon emission efficiency level of Enshi Tujia and Miao Autonomous Prefecture in the west rose from 0.42 in 2010 to 0.94 in 2020, averaging 0.60, and has jumped into the high-level region since 2013. Enshi implements the development philosophy of ‘establishing the province on ecology, developing the province through industry, and revitalizing the province through opening up’, actively promoting local characteristic agriculture, eco-cultural tourism, and new resource-based industries to achieve leapfrog development in scientific development. Enshi Autonomous Prefecture ranks first in the province, with 58% of its area designated red lines for ecological protection. It has gradually transformed its ecological advantages into economic and industrial advantages, laying a solid foundation for sustainable development.

An in-depth examination of the regional distribution of alterations in carbon emission efficiency in Hubei Province from 2010 to 2020 shows that the overall carbon emission efficiency in Hubei Province is on the rise (Figure 7). Wuhan, Xiantao, Enshi Autonomous Prefecture, and Shennongjia Forest District have seen the most rapid growth, followed by Tianmen, while Jingmen City has seen a slight decline. With the improvement of the city’s energy level, scientific and technical innovation in Wuhan is consistently advancing. Enshi and Shennongjia are important ecological protection areas; Xiantao is committed to creating a national forest city and promoting the ‘Green Xiantao’ initiative, and Tianmen is deeply implementing the ‘Blue Sky, Clear Water, Pure Land’ project to improve the ecological environment continuously. The coordination between Jingmen City’s environmental protection and green development requirements is not yet complete.

3.2. Empirical Study into the Influence of Green Technological Innovation on Carbon Emission Efficiency in Hubei Province

3.2.1. Variable Selection

We examine the factors of carbon emissions and look at research ideas from the relevant literature [50]. To show how complete the indicators are and how important each factor is, we use carbon emission efficiency (CV) as the dependent variable to explain carbon emissions from the economic, social, and environmental points of view. Green technology innovation (GI) is used as the independent variable [51]. Patents are the source of technological innovation [52]. Many experts utilize patent data to assess the degree of regional creativity. This report incorporates metrics of capital investment and human capital to depict the extent of innovation in green technologies thoroughly. An index system exists for the elements influencing carbon emission efficiency in Hubei Province (

Table 2. Variable indicators of carbon emission efficiency in Hubei Province.

Indicator Attributes	Indicator Name	Indicator Explanation
Variable being explained	Carbon emission efficiency (CV)	Carbon emission efficiency value
Explanatory variable (green technology innovation GI)	Financial input (FI)	R&D expenditure as a proportion of GDP [57]
	Human capital (HC)	Number of R&D personnel [50]
	Technological achievements (TA)	Number of green invention patents + green utility model patents authorized [58]
Control variable	Level of economic development (ED)	Per capita GDP
	Industrial structure (IS)	Secondary industry output value/GDP
	Population density (PD)	Total population/area
	Government environmental regulation (GR)	Industrial wastewater, SO2, and smoke and dust per unit of output

). The control factors include the amount of economic development, population density, industrial structure, and environmental regulation [53,54]. Green technological innovation is characterized by capital investment, human capital, and technological achievements. In theory, increasing green technological innovation is conducive to reducing consumption and pollution. Per capita GDP expresses the degree of economic advancement and measures the quality of regional development. The industrial structure has a significant demand for energy and is regarded as the primary producer of carbon dioxide emissions [55]. The level of population density affects carbon emissions through production and life [56]. Government environmental regulations have a specific effect on corporate pollution emissions.

3.2.2. Model Calculation and Results

• Stability test

The study used the HT and IPS methods to perform unit root tests on the variable data to ensure the regression analysis was correct and to avoid false regression in the data calculations. The test findings validated the data’s stability and absence of a unit root, enabling the performance of regression analysis (

Table 3. Smoothness test for panel data.

Variable Value	HT Statistic	p Value	IPS Statistic	p Value	Conclusion
lnCV	−0.0439	0.0000	−4.2588	0.0000	stationary
lnFI	0.0520	0.0001	−5.5382	0.0000	stationary
lnHC	−0.0139	0.0000	−4.6698	0.0000	stationary
lnTA	−0.0348	0.0000	−4.7410	0.0000	stationary
lnED	−0.7530	0.0000	−4.2264	0.0000	stationary
lnIS	−0.0063	0.0000	−4.6453	0.0000	stationary
lnPD	0.0686	0.0000	−3.3927	0.0003	stationary
lnGR	0.0711	0.0002	−4.9292	0.0000	stationary

).

2.

Full sample regression

We utilized personal fixed-effect models, period fixed effects, random effects, and two-way fixed-effects models to analyze the influence of the efficiency of carbon emissions in 17 prefecture-level areas in Hubei Province between 2010 and 2020. The Hausman test and the regression-adjusted R² and F statistics were utilized to ascertain the optimal regression outcomes of the two-way fixed-effects approach that most appropriately fit the data. These results were then used for a more in-depth analysis (

Table 4. Full-sample baseline regression.

	Individual Fixed-Effect Model	Time Fixed-Effect Model	Random-Effects Model	Two-Way Fixed-Effect Model
lnFI	0.0569 ***	−0.0957 ***	−0.0849 ***	0.0392 **
	(0.0195)	(0.0336)	(0.0327)	(0.0190)
lnHC	0.0576 **	−0.0920 **	−0.0978 **	0.0491 *
	(0.0279)	(0.0419)	(0.0408)	(0.0272)
lnTA	0.0496 **	0.1846 ***	0.1773 ***	0.0605 ***
	(0.0198)	(0.0388)	(0.0358)	(0.0198)
lnED	0.2671 ***	−0.0073	−0.0154	0.2763
	(0.0697)	(0.0778)	(0.0675)	(0.2399)
lnIS	−0.1355	0.4017 **	0.3696 **	−0.0515
	(0.1126)	(0.1597)	(0.1522)	(0.1206)
lnPD	0.0313	0.1784 ***	0.1821 ***	0.1485
	(0.1673)	(0.0383)	(0.0375)	(0.1824)
lnGR	−0.0945 ***	−0.1155 ***	−0.1079 ***	−0.0722 ***
	(0.0235)	(0.0361)	(0.0281)	(0.0241)
cons	−2.3598	−1.0236	−0.9114	−2.9845
	(1.4287)	(1.0189)	(0.9381)	(3.6569)
R2	0.4415	0.3879		0.9244
adj. R2	0.3627	0.3263		0.9082
F	18.4079	15.2969		81.3960

Note: ***, ** and * denote significance thresholds of 1%, 5%, and 10%; the value in brackets is the standard error of the clustering robustness; cons represent the constant term.

).

The correlation coefficient between financial investment (FI) and carbon emission efficiency in green technological innovation is 0.0392. This is significant at the 1% confidence level, suggesting that augmenting expenditure in research and experimental development can substantially enhance carbon emission efficiency. Augmenting investment in research and development while fostering nascent sectors like advanced energy storage and sustainable low-carbon construction can improve the technical innovation capacities of firms. The correlation coefficient between human capital (HC) is 0.0491. This is significant at the 10% confidence level, suggesting that enhanced scientific research can facilitate the comprehensive development of fresh electricity and carbon capture and storage technologies to augment energy efficiency. The correlation coefficient for technological achievements (TA) is 0.0605, effectively satisfying the significance criterion at the 1% level. The conversion and application of green invention and utility model patent results have promoted low-carbon innovation, including innovation in low-carbon materials and production processes. These innovative technologies help to achieve lower carbon emissions.

The innovation elements of green technology strongly impact carbon emissions (Figure 8). Using research funding, human capital, and green patents as input factors, the study and growth of green technology facilitates the transformation and application of innovative outcomes, improves waste recycling and reuse, and is conducive to sustainable development. Bioenergy carbon capture and storage, geological utilization of carbon dioxide and other bio-carbon sequestration, and harmful carbon technologies, such as the efficient conversion into fuel chemicals, can be enhanced by green management. This can improve the quality of sustainable production and energy efficiency, facilitating the evolution and advancement of the industrial structure, achieving the goal of high-quality development, and enhancing manufacturing efficiency effectively. The innovation of green technology has reduced the cost and price per unit product, stimulated market demand for green products, and thus resulted in increased energy use. Simultaneously, it has also brought certain social benefits, enhanced residents’ environmental awareness, and, therefore, effectively improved carbon emission efficiency.

The coefficient of association between economic development (ED) and carbon emission efficiency across the control variables is 0.2763. This indicates that increased economic development can stimulate technological innovation, optimize market allocation, and advance economic growth to an innovation-driven model. The correlation coefficient between industrial structure (IS) is −0.0515, which is negatively correlated. In rapid economic development, extensive development of the secondary industry, hefty and energy-intensive industries, will increase carbon emissions. The correlation coefficient for demographic density (PD) is 0.1485. A rise in the number of people fulfills the need for local labor, is conducive to gathering high-quality talent, and injects innovative impetus into regional economic development. The notion of low-carbon development is constantly being refined, and a shift in public governance can promote low-carbon lifestyles and consumption patterns, consequently enhancing carbon emission efficiency. The correlation coefficient for government environmental regulation (GR) is −0.0722, negatively correlated and significant at the 1% credibility level. This indicates the government’s strengthening of ecological governance, formulation of relevant regulations and policies, and strengthening of punishment for environmental pollution, as well as the approval of environmental impact assessments and pollutant discharge permits for construction projects, facilitating the synchronized advancement of pollution mitigation and carbon reduction.

3.

Empirical analysis of time lags

The influence of green technological innovation affects emissions of carbon efficiency; starting from the input, technological innovation needs to go through certain research and development stages to achieve results, which can then be applied in real life to increase productivity and promote carbon emission efficiency. Therefore, there exists a certain time lag in the growth of green technological innovation, referred to as the lag effect.

The empirical analysis in the previous article is a static panel that does not consider the lagged terms of the dependent variable. Current inputs affect current variables and the first, second, and longer-term lags of variables. This research develops a dynamic panel data model (GMM) to investigate the effect of green technology innovation on carbon emission efficiency, using the lagged third order of the indicators of green technological innovation, namely capital investment (FI), human capital (HC), and technological achievements (TA), to investigate the delayed impact of green technological progress.

Table 5. The lagged effects of capital investment (FI), human capital (HC), and technological achievement (TA) on carbon emission efficiency.

Variable	Financial Investment (FI)				Human Capital (HC)				Technological Achievement (TA)
	Current	Lag One Period	Lag Two Period	Lag Third Period	Current	Lag One Period	Lag Two Period	Lag Third Period	Current	Lag One Period	Lag Two Period	Lag Third Period
lnFI	0.149	0.296	0.237	0.247 ***	0.149	−0.024	0.158	0.279	0.149	−0.059	0.224	0.254
	(0.83)	(1.14)	(0.68)	(6.05)	(0.83)	(−0.09)	(0.5)	(0.93)	(0.83)	(−0.36)	(1.08)	(1.16)
lnHC	−0.057	−0.035	−0.051	−0.129	−0.057	−0.09	−0.084	0.094	−0.057	0.066	0.073	0.093
	(−0.99)	(−0.45)	(−0.48)	(−1.52)	(−0.99)	(−0.87)	(−0.53)	(0.43)	(−0.99)	(0.57)	(0.72)	(0.49)
lnTA	0.111 *	0.06	0.041	0.219	0.111 *	0.095	0.036	−0.041	0.111 *	0.004	−0.133	−0.037
	(1.76)	(0.33)	(0.18)	(1.46)	(1.76)	(0.49)	(0.19)	(−0.21)	(1.76)	(0.03)	(−1.11)	(−0.50)
lnED	0.139	−0.089	0.113	1.134 ***	0.139	0.2	0.133	−0.155	0.139	0.132	−0.358	−0.335
	(0.75)	(−0.25)	(0.25)	(2.9)	(0.75)	(0.56)	(0.37)	(−0.28)	(0.75)	(0.32)	(−0.59)	(−0.59)
lnIS	0.893	0.397	0.318	2.376 *	0.893	−0.216	0.37	0.264	0.893	0.093	0.725	0.637
	(0.95)	(0.38)	(0.25)	(1.72)	(0.95)	(−0.24)	(0.22)	(0.32)	(0.95)	(0.15)	(0.66)	(0.57)
lnPD	0.06	0.466	0.393	−0.708	0.06	0.392	0.32	0.749	0.06	0.369	0.508	0.581
	(0.2)	(1.52)	(0.61)	(−1.15)	(0.2)	(1.15)	(0.58)	(1.31)	(0.2)	(1.42)	(1.58)	(1.49)
lnGR	−0.052	−0.121 **	−0.084	0.174	−0.052	−0.128	−0.068	−0.123	−0.052	−0.056	−0.158	−0.165
	(−0.48)	(−2.16)	(−0.84)	(0.88)	(−0.48)	(−1.30)	(−0.61)	(−0.84)	(−0.48)	(−0.38)	(−0.94)	(−1.03)
L.		−0.176	−0.142	0.505		−0.063	−0.095 *	−0.032		0.074	0.109	0.072
		(−0.70)	(−0.57)	(1.22)		(−0.59)	(−1.72)	(−0.13)		(0.57)	(0.67)	(0.63)
L2.			−0.047	−0.274			0.071	0.195			0.185	0.068
			(−0.14)	(−1.36)			(0.34)	(0.6)			(1.05)	(0.57)
L3.				−0.872 *				0.035				0.021
				(−1.92)				(0.13)				(0.27)
Constant	−0.315	−1.985	−2.563 ***	−1.622	−0.315	−3.779 **	−1.967	−1.11	−0.315	−3.003 **	−0.625	−0.79
	(−0.16)	(−1.54)	(−3.28)	(−0.49)	(−0.16)	(−2.05)	(−0.48)	(−0.51)	(−0.16)	(−2.04)	(−0.23)	(−0.34)
Observations	170	170	153	136	170	170	153	136	170	170	153	136
Number of cities	17	17	17	17	17	17	17	17	17	17	17	17

z-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

displays the regression results. The current period’s impact coefficient of financial investment (FI) on carbon emission efficiency is 0.149. This is in line with the benchmark regression results of the whole sample, which show a positive correlation. The impact ratings of the first-order and second-order lag factors are negative and fail the significance test. This indicates that financial investment has a stimulating effect in the current period, but the influence weakens over time and eventually has a negative impact. The R&D funds of enterprises or research institutions directly contribute to the development and application fields, so the lag period is relatively short. Consequently, the allocation of R&D funds positively stimulates firms’ R&D operations in the present period, encouraging them to engage in increased green innovation and attain specific economic and ecological advantages.

The coefficient of human capital (HC) effect on the efficiency of carbon emissions is −0.057 at the current time, −0.063 in the first lag period, 0.071 in the second lag period, and 0.035 in the third lag interval. This analysis shows that human capital negatively impacts the current and first lag periods. The sizeable initial accumulation of R&D personnel may increase energy consumption, but the effect turns positive in the second and third lag periods. In the current period and the first-order lag period, newly added R&D personnel may face problems of resource misallocation. Newly joined R&D personnel need time to adapt to the enterprise’s working environment and R&D process, and inefficiency may occur during this period [59]. Secondly, green technological innovation requires a certain amount of knowledge accumulation and technological transformation time. In the current and first-order lag periods, newly joined R&D personnel may still be conducting basic research and knowledge accumulation and have not yet entered the stage of substantive innovation. With time (lag periods 2 and 3), the knowledge and experience accumulated by these personnel will gradually be transformed into actual innovative achievements, hence exerting a positive effect on green technology innovation [60].

The influence coefficient of green technology achievements (TA) and green patent authorizations on carbon emission efficiency is presently 0.111, which is substantial at the 10% level and has the exact correlation as the benchmark regression outcomes for the entire sample. The influence coefficients of the first, second, and third lag terms are 0.074, 0.185, and 0.021, in that order, indicating a positive contribution. However, their influence has diminished compared to the current impact coefficient. Some emerging industries have rapid technological upgrades and depend highly on research and development activities. Hence, the conversion efficiency between their research and development investment and output is high, and the lag period is relatively short.

4.

Endogeneity test and robustness test

To avoid the problem of endogeneity of capital investment, human capital, and technological achievements, the study assumes that all variables are exogenous and conducts a Hausman test. The findings demonstrate that the p-value is 1, leading to the acceptance of the original hypothesis; that is, it is concluded that there is no endogeneity. To make sure the research results were reliable, the study chose and changed the main explanatory variables, used the lagged one-period green technology innovation index as the instrumental factor, and performed a two-stage least squares test on the data (

Table 6. Robustness test results.

Variable	Benchmark Regression	Explanatory Variable One Period Lag	Replacement of Core Explanatory Variable—Number of Green Patent Grants
lnFI	−0.1131 ***	−0.1093 ***
	(0.0343)	(0.0329)
lnHC	−0.0197	−0.0276
	(0.0240)	(0.0250)
lnTA	0.1314 ***	0.1561 ***	0.0944 ***
	(0.0438)	(0.0512)	(0.0341)
lnED	0.0181	0.0312	0.0213
	(0.0778)	(0.0910)	(0.0807)
lnIS	−0.4257 ***	−0.4069 **	−0.2640 *
	(0.1481)	(0.1634)	(0.1430)
lnPD	0.2015 ***	0.2087 ***	0.2386 ***
	(0.0444)	(0.0449)	(0.0388)
lnGR	−0.1111 ***	−0.1364 ***	−0.1500 ***
	(0.0294)	(0.0312)	(0.0286)
cons	−1.9811 ***	−1.8911 ***	−1.2294 ***
	(0.3288)	(0.3508)	(0.3180)
N	170	170	187
R2	0.3914	0.419	0.3452
adj. R2	0.3651	0.3939	0.3271
F	12.5217	14.3693	12.2808

z-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

). The results indicate that each explanatory variable’s significance levels and impact characteristics are consistent and accompanied by the benchmark regression findings, indicating that the robustness of test outcomes is reliable.

4. Discussion

4.1. Time Series of Carbon Emission Efficiency in Hubei Province

The carbon emission efficiency in Hubei Province is exhibiting an upward trajectory, aligning with prior studies [61,62]. The carbon efficiency rating rose from 0.36 in 2010 to 0.39 in 2015, reflecting a median annual growth rate of 1.61%; 2015–2020 was a period of rapid recovery, with the carbon emission efficiency value increasing from 0.39 in 2015 to 0.54 in 2020, a mean yearly growth rate of 6.72%. The peak of the kernel density curve for carbon emission efficiency in Hubei Province moved briefly to the left and then to the right. The efficiency of carbon emissions initially declined before progressively rising; the highest point diminished as the width expanded. No discernible tailing was observed, and regional differences in carbon emission efficiency exhibited a progressive expansion trend. Still, they were relatively concentrated each year, and there was no apparent polarization trend.

4.2. Spatial Differences in Carbon Emission Efficiency in Hubei Province

Spatial variations in carbon emission efficiency exist within Hubei Province. The degree of differentiation between the whole province, the Wuhan metropolitan area, and the ‘Xiangshi Suishen’ urban agglomeration shows an increasing trend, while the differentiation within the ‘Yi Jing Jing En’ urban agglomeration gradually decreases. Existing research on carbon emission efficiency across Chinese provinces also reveals significant spatial disparities [63]. The degree of overlap between the carbon emission efficiencies among the three major urban agglomerations across different regions is primarily responsible for the increased volatility of the overall difference. Since 2019, the Gini coefficient of carbon emission efficiencies between urban agglomerations has slightly decreased.

4.3. Mechanism of the Impact of Green Technological Innovation on Carbon Emission Efficiency in Hubei Province

Green advancements in technology positively impact carbon emission efficiency in Hubei Province. An enhanced level of green technological innovation can stimulate economic growth while reducing carbon emissions, improving efficiency, and facilitating high-quality development [64]. Among the control variables, improving economic development level and population density helps promote low-carbon, high-quality development, and government environmental regulations and industrial structure are negatively correlated with carbon emission efficiency. Existing research has indicated that rationalizing and upgrading the industrial structure can enhance the influence of green technological innovation on urban reductions in carbon dioxide [65], and carbon emission efficiency can be enhanced through strengthened environmental regulation [66]. Consequently, strengthening governmental ecological regulation and optimizing industry structure can improve the efficiency of carbon emissions.

4.4. The Influence of Green Technological Innovation on Carbon Emission Efficiency in Hubei Province Exhibits a Temporal Lag

The influence of green technological innovation on emissions of carbon efficiency in Hubei Province exhibits a certain time lag. Previous research has demonstrated that green technical innovation may have a more profound long-term influence on carbon emissions [18]. However, most of these studies analyze this impact from the perspective of overall effects and their quadratic terms as proxies for long-term consequences, missing detailed discussions of specific indicators and multiple lag periods. The analysis in this paper reveals that capital investment positively affects carbon emission efficiency in the present time, although it exerts an adverse effect in lagged periods. Human capital exerts a positive impact in the second and third lag periods. At the same time, technological advancements enhance emissions of carbon efficiency across the whole cycle, with the most significant effect occurring in the current period.

Based on the preceding analysis, to attain green, low-carbon, and environmentally friendly growth in Hubei Province, it is essential to augment investment in development and research and fortify the foundation for innovation. Strengthening talent training in the ‘dual carbon’ field is also crucial. Furthermore, we should enhance policy mechanisms to boost the efficiency of innovation. Specifically, the Wuhan metropolitan area could set up a special fund for low-carbon technology innovation, focusing on supporting research and development projects in green technologies such as new energy, energy conservation, and environmental protection. We should encourage universities and vocational colleges to introduce ‘dual carbon’ related majors and courses for professional training. At the same time, an access system for high-energy-consuming and high-emission projects needs to be strictly implemented, and ‘dual control’ management of energy and water consumption needs to be strengthened. The ‘Xiangshi Suishen’ urban agglomeration can focus on advantageous industries such as the automobile industry, augment investment in development and research in sectors such as renewable energy vehicles and intelligent linked cars, and improve industrial competitiveness. Relying on the resources of universities to cultivate high-quality talent adapted to the ‘dual carbon’ goal, they can carry out cross-regional talent exchange activities and introduce high-level ‘dual carbon’ skills. The construction plan for the ‘dual carbon’ science and technology pilot area for innovation applications in the Han River Ecological Economic Belt needs to be implemented. Regional collaborative governance must also be strengthened, and projects that use a lot of energy and produce pollution must be strictly controlled. The ‘Yi-Jing-Jing’en’ urban agglomeration should prioritize the advancement of green transformation technologies in conventional sectors, encompassing the chemical and construction materials sectors. Enhance environmental oversight of traditional sectors, including the chemical and construction materials industries; facilitate the green transition of these businesses; and execute the objectives of reaching peak carbon emissions and attaining neutrality in carbon emissions.

5. Conclusions

This study utilized the super-efficient SBM model to assess the carbon emission efficiency of Hubei Province from 2010 to 2020 and examined its historical and regional differentiation characteristics. In addition, the impact of green technology innovation and its lagged effect on carbon emission efficiency was explored using benchmark regression and dynamic GMM models to establish a scientific foundation for Hubei Province to formulate a reasonable reduction of carbon emissions. The findings indicate that Hubei Province’s carbon emission efficiency fluctuated between 2010 and 2015, exhibiting a median annual growth rate of 1.61% and a swift recovery from 2015 to 2020, characterized by a median yearly expansion rate of 6.72%. The disparities in carbon emission efficiency show a trend of gradual expansion, but there is no apparent polarization trend. There are specific spatial differences in carbon emission efficiency in Hubei Province. The extent of overlap in carbon emission efficiencies among the three principal urban agglomerations across various regions constitutes the primary cause of the overall rise in volatility. Green technology innovation positively impacts Hubei Province’s carbon emission efficiency.

Regarding the control variables, enhancing economic development and population density helps promote low-carbon, high-quality development. At the same time, government environmental regulation and industrial structure exhibit a negative correlation with carbon emission efficiency. In addition, the influence of green technological innovation on carbon emission efficiency is subject to a temporal lag. Capital investment and technological achievements have the most significant positive impact in the current period, while human capital exerts a beneficial influence in the second and third lags.

Future studies will further explore the correlation between carbon emission efficiency and innovation in green technology. Firstly, static and dynamic methods will be used to analyze green technological innovation’s heterogeneous and time-lag effects on carbon emission efficiency in urban agglomerations or prefecture-level cities. Secondly, comparisons between Hubei Province and other provinces will be strengthened to analyze the disparities in carbon emission efficiency and green technological innovation between different provinces. Thirdly, carbon emission efficiency is influenced by elements outside socio-economic considerations and constrained by natural conditions. At the same time, all human production and life take place on land. Subsequent land use type changes will be considered to elucidate the correlation between carbon emission efficiency, land use change, and green technological innovation at multiple scales. While analyzing the current situation, scenario simulations and predictions can be further carried out for carbon emission efficiency, land use change, and green technology innovation in different urban agglomerations or prefecture-level cities to propose a more comprehensive and forward-looking carbon emission reduction strategy to enhance the environmentally friendly and sustainable development of Hubei Province’s economy and ecological protection.