1. Introduction
Since the industrial revolution, the industrial economy with high energy consumption and high emissions has grown rapidly and has become the main driving force for the sustained growth of the world economy. However, long-term dependence on the industrial economy to promote economic growth will inevitably consume a large amount of fossil energy, resulting in a continuous increase in total carbon emissions [
1]. Since the reform and opening up, the two-wheel drive of industrialization and urbanization has promoted the rapid growth of China’s economy, from 1979 to 2016, the average annual growth rate of GDP was as high as 15%. Statistics show that in 2000, China’s per capita carbon emissions were 2.697 tons, while in 2011, per capita carbon emissions increased by nearly three times, reaching 7.242 tons. China’s total energy consumption in 2016 was 4.36 billion tons of standard coal, and its carbon dioxide emissions reached 10.21 billion tons in an unprecedented way, making it the world’s largest carbon dioxide emitter. The continuous accumulation of carbon emissions creates a range of environmental problems, such as global warming, rising sea levels and frequent outbreaks of extreme weather. From the birth of the
United Nations Framework Convention on Climate Change to the signing of the
Kyoto Protocol and the convening of the
Copenhagen Climate Conference, a series of environmental problems caused by carbon emissions have received increasing attention from all countries. Therefore, carbon emission reduction and climate change have become important issues in today’s world. However, population and climate change are closely related [
2]. Factors such as population size, population age structure, gender structure, population urbanization, economic development and industrial restructuring will have an impact on climate change and carbon emission reduction. The organic combination of climate change and gender equality also raises many important research issues. Research by Cook et al. shows that in the process of dealing with climate change, gender quotas should be introduced to allow more women to participate in climate policy formulation, which not only promotes gender equality, but also improves policy effectiveness [
3]. Therefore, from the perspective of the synergy between GDP and gender factors, this paper has an important practical significance to indirectly explore the effects of carbon emissions.
The existing literature has some empirical research on carbon emissions and its influencing factors, and scholars are expected to control the corresponding factors to achieve emission reduction effects. However, most scholars explore the influencing factors of carbon emissions from the perspective of direct effects, and less literature discusses the indirect effects of gender factors on carbon emissions. Scholars mainly studied the effects of energy structure, energy intensity, industrial structure, total output value and population factors on carbon emissions [
4,
5,
6,
7,
8,
9,
10,
11,
12]. Japanese scholar Kaya believes that the driving force of carbon emissions in a country or region mainly includes four major factors, namely, population, per capita GDP, energy consumption per unit of GDP, carbon emissions per unit of energy consumption, and the famous Kaya formula is proposed [
13]. Among them, the relationship between economic growth and carbon emissions cannot be ignored. Scholars combine macroeconomic issues such as economic growth, industrial structure and total factor productivity with carbon emissions, and incorporate carbon emission issues into the model, while extending some related concepts, such as green total factor productivity and green GDP [
14,
15]. Economic growth affects the total amount of carbon emissions, but in turn, the amount of carbon emissions reflects the degree of economic development, there is a mutual two-way causal relationship between the two, and actively developing a green economy can effectively reduce carbon dioxide emissions. With the rapid economic growth, the marginal carbon emissions show a downward trend, and the economic aggregates become the main driving factor for the increase of carbon emissions. At the same time, the economic growth rate contributes 42.9% to China’s per capita carbon emissions, showing an exponential growth [
16,
17].
As the relationship between population and resources and the environment becomes more prominent, population factors are one of the main factors affecting carbon emissions. The main way influencing the impact of demographic changes on carbon emissions is the abundant supply of labor in the production sector [
18], a large number of studies have focused on the analysis of population size, economic level, technology and industrial structure using the STIRPAT model, and the results show that population size is one of the most important factors affecting carbon emissions [
19,
20]. Li et al. found that the population has a significant positive impact on carbon emissions, and there is a long-term, stable relationship [
21]. Wang et al. also found that the population size of most provinces has a positive impact on carbon emissions [
22], this is consistent with most research findings. The population ageing and gender factors in the population structure also have a significant impact on carbon emissions. The aging of the population has a negative effect on carbon dioxide emissions, and the acceleration of population aging has a negative effect on long-term carbon emissions [
23]. In the study of the impact of population factors on carbon emissions, it is a very important research perspective to examine the root causes of climate change based on gender structure. Men’s “carbon footprint” is much higher than that of women, mainly due to differences in consumption and risk perception between women and men [
24], and the impact of gender differences on carbon emissions is also related to the level of economic and social development [
25]. Women are not only vulnerable groups in climate change, but women’s lifestyles and concepts can play a positive role in reducing emissions, and carbon emissions are sensitive to changes in gender structure, carbon emissions are significantly negatively correlated with female population [
2].
Through in-depth analysis of domestic and foreign literature on carbon emissions related research, it is found that in terms of research methods, there are mainly two types of literatures: non-spatial features and spatial features. In the study of factors affecting carbon emissions without spatial characteristics, the research methods mainly include the logarithmic mean weight Divisia decomposition method (LMD) [
26,
27], the structural decomposition analysis method (SDA) and input and output (IO) method [
28,
29], the Kaya equation and the modified STIRPAT method [
30,
31,
32], and the Environmental Kuznets Curve (EKC).
With the strengthening of inter-provincial cooperation, especially the establishment of economic circles, economic belts, population cross-regional mobility, and the addition of spatial factors, the relationship between gender and carbon emissions can be studied more effectively. In the study of carbon emission factors including spatial features, two methods are mainly considered. One method is the measure of spatial linkage characteristics, mainly the spatial correlation analysis (Moran’s I index). Fu et al., Ma et al., Cheng et al. and Wu et al. used this method to study the relationship between carbon emissions and carbon emission intensity in various provinces, and analyzed the changing relationship between carbon emissions in various provinces [
9,
33,
34,
35]. Spatial correlation analysis can calculate the relationship between different regions in a more intuitive way, and it is also an important basis for judging whether the model is added to spatial features. In addition, the LISA map provides a clear analysis of the relationship between local carbon emissions. Another method is a model that contains spatial effects in the model. The spatial effect model is mainly based on the analysis of spatial correlation. By observing the results of spatial correlation analysis to determine whether the carbon emissions have spatial correlation between regions, that is, the absolute value of the Moran’s I index is not equal to zero. In the model containing spatial effects, there are mainly two parts, and one part is the setting of the spatial weight matrix. There are many ways to set spatial weight matrix, including proximity, distance, and economic relationships. The other part is the spatial measurement model, which can be divided into four categories, namely spatial lag model (SAR or SLM), spatial error model (SEM), spatial Durbin model (SDM) and spatial Durbin error model (SDEM). Ma et al. compared the analysis results of SAR and SEM with common panel models and found that models with spatial effects can be better explained [
34]. Cheng et al. used SDM model to study the influencing factors of China’s energy consumption carbon emission intensity [
9]. Based on the STIRPAT model and the dynamic spatial Durbin panel data model, Niu and Liu empirically analyzed the influencing factors of China’s construction industry carbon emissions based on the 2002-2013 provincial panel data [
36]. Zhao et al. measured the dynamic trend and agglomeration characteristics of carbon emission intensity in China from 2000 to 2015 by nuclear density distribution and Moran index, and analyzed the main influencing factors by using the spatial Durbin model [
37].
The existing literature provides valuable references for the research of this paper. Based on the existing literature, there is still room for improvement in the impact of carbon dioxide emissions from the perspective of GDP and gender-based synergies: (1) The analysis of the influencing factors of carbon emissions in the existing literature is mainly attributed to energy structure, energy intensity, industrial structure, total output value and population factors, less interaction between gender factors and GDP is introduced, and the research scales are mostly the national level or the provincial level. However, due to historical and market economic development, there are significant differences in various factors such as economy, industrial structure, technological level, energy consumption and environment in various regions of China. In order to further carry out carbon emission research, this paper introduces gender factors and gender factors and GDP interactions at the national and regional levels, and then deeply study the effects of carbon emissions. (2) In the existing literature, there are few choices for spatial weight matrix, and most of the literature adopts the method of subjective choice determination, which cannot reflect the true relationship optimally. Sometimes it may be caused by improper selection of weight matrix. The model results in a bias. There are also subjective choices for the selection of spatial econometric models. The model SDM with SAR and SEM structures is generally selected as the analytical model, so that although the effects of different structures of different variables on carbon emissions can be comprehensively analyzed, SDM does not include SDEM model structure, if the real data structure conforms to SDEM or other non-SDM data structures, the use of SDM structures may over-interpret certain variables. (3) With the emphasis on dynamic issues by scholars, there are also selection problems in the data form of spatial measurement models. Which data types in static panel and dynamic panel data types are more able to reflect the meaning of variables? There are few references about the selection of this type of data.
Based on the above research, this paper discusses the impact mechanism of economics, gender factors and gender-based synergies on carbon emissions from the national level and regional levels, and sets up two data types, four alternative spatial measurement models and 12 kinds of optional spatial weight matrix, a total of 96 model structures, using Bayesian posterior probabilities and data-driven methods to select the optimal spatial weight matrix and econometric model, reducing the possibility of inappropriate selection of model structure due to subjective factors, improve the accuracy of the analysis results. The choice of control variables not only considers the gender factors in the population structure, but also selects factors such as aging factors, economic factors, industrial structure, and household consumption, fixed asset factors. It adds the interaction between economic and gender factors in a spatial perspective, from different levels. A more in-depth analysis of the impact of economics, gender factors and gender-based synergies effect on carbon emissions.