1. Introduction
As global issues like environmental deterioration and climate change resulting from economic growth have gotten worse in recent years, nations have come to understand the significance of sustainable and green development. The United Nations incorporated sustainable development into its long-term plan through the Agenda for Sustainable Development 2030, which includes SDG 17—the Sustainable Development Goals—following the Kyoto Protocol and the Millennium Development Goals (MDGs). China, the biggest developing nation in the world, has declared its 2020 “peak carbon” and “carbon neutrality” goals. However, China is also undergoing a transition from rural to urban areas, and concerns about how to balance environmental preservation with economic growth are mounting. A key component of long-term economic growth is innovation [
1,
2]. Because of its qualities that safeguard the environment, green innovation, as opposed to traditional innovation, may meet the needs of green and sustainable economic development [
3]. Consequently, encouraging green innovation is essential to accomplishing sustainable development goals and resolving the conflict between superior economic growth and environmental preservation [
4]. The primary characteristic of green innovation is thought to be the “double externality” [
2]. While the benefits of innovation can be seen in the preservation of the environment rather than in the firms themselves, the positive environmental externality allows the R&D results, in which firms have invested at a high cost, to be copied and used by others at a low cost. The incentives for businesses to invest in green innovation are impacted by both externalities [
5]. It is clear that the characteristics of green innovation make it difficult for supply-side innovation subjects to fully benefit from innovation, resulting in input potency deficiencies that, to some extent, impede the advancement and deployment of green innovation [
6]. Businesses are primarily driven to innovate by their need to make money [
7], and they are only motivated to introduce green ideas into the market when they see a return [
8]. This is in contrast to the supply side, where demand-side needs drive expected returns. Additionally, the expansion of the market’s needs, as well as the raising of standards for both demand and consumption, presents businesses with new chances to investigate creative activities and rewards, which furthers the advancement of green innovation. Therefore, in order to better support the expansion of the high-quality economy and achieve the goal of green sustainable development, it is necessary to promote green innovation at this time. This can be realized by depending on the demand for green innovation to form an effective support system. This research aims to provide a thorough analysis of the demand factors influencing green innovation, taking into account the history mentioned above.
Since the 1960s, the “demand-pull” theory has gained prominence, with Schmookler suggesting that the role of demand in innovation cannot be overlooked and presenting the viewpoint of “demand-induced innovation” [
9]. In the 21st century, demand factors have increasingly attracted the attention of scholars. Numerous studies have claimed that demand factors can stimulate the development of green innovations [
10,
11,
12]. However, contrasting conclusions have been drawn in some of the literature [
13].
Although the academic community has developed some understanding of how demand influences green innovation, current research often overlooks income distribution due to the influence of the “consumer homogeneous preference” assumption. Foellmi and Zweimüller [
14] have emphasized the central role of income distribution in shaping innovative product markets and profitability. Therefore, when discussing the impact of demand on green innovation, the influence of the demand structure is essential. In addition, existing research frequently uses survey data, often from developed countries and regions [
15,
16,
17]. While survey data provide more detailed insights, using panel data for research can better capture dynamic factors and mitigate omitted-variable problems. Different countries are at different stages of economic and social development, leading to differences in the size and structure of demand between developed and developing countries. Therefore, region-specific research is essential [
18]. It is worth noting that innovation activities exhibit spatial effects, with neighboring regions’ green innovation activities and influencing factors affecting local green innovation [
5,
19]. Market segmentation exists as a means for local governments in China to pursue economic interests. However, the existing literature often neglects the adverse effects of regional market segmentation on resource allocation efficiency and enterprise innovation, which can exert a more pronounced inhibitory influence on the corporate innovation drive [
20].
In summary, to address the existing research limitations, this study delved into research using data from Chinese prefecture-level cities spanning the years 2008 to 2020, which represents a finer-grained scale than previous studies, with the purpose of addressing the following research objectives: First and foremost, the aim of this research was to investigate how domestic demand structural variables, international demand, and domestic demand may affect the advancement of green innovation. Secondly, when relevant factors were found to influence the growth of green innovation in China, this study looked at whether there is variability in the region or patent type. Third, an initial examination of the implications of market segmentation was carried out, and the spatial spillover effects of demand determinants in driving green innovation were explored.
This paper makes several noteworthy innovative contributions: First and foremost, this study offers a comprehensive examination of the impact of demand on green innovation. The paper categorizes the influence of demand factors into “domestic demand” and “foreign demand”, further dissecting “domestic demand” into the “domestic demand level” and “domestic demand structure”. Secondly, this research leverages data from cities in a developing country. Given that green innovation is intrinsically tied to the distinct economic and social contexts of different countries [
21,
22], the utilization of Chinese urban data in this study provides valuable insights for promoting green innovation in developing countries. Lastly, this paper delves into the impact of demand factors on green innovation from a spatial spillover perspective. Taking into consideration the backdrop of China’s factor market reform and the advantages associated with using urban data, the paper aims to introduce market segmentation variables to investigate the potential moderating role of market integration when demand factors influence green innovation. This offers fresh insights into how to effectively promote green innovation, starting from the demand side.
5. Further Exploration of Spatial Effects
Some studies have found that green innovation exhibits spatial spillover effects [
5,
19]. Therefore, building upon the baseline regression, this study employed spatial econometric models to analyze the spatial spillover effects of demand factors on green innovation levels. Given that the spatial Durbin model considers both spatial lag in the dependent variable and spatially autocorrelated error terms, this study opted for the Spatial Panel Durbin Model for estimation. Taking into account the factors mentioned above and building upon the study by Duan and Xia [
58], in order to identify the demand spillover effects, this research used the Spatial Panel Durbin Model. Equation (5) provides the specific formulation of the model:
In the specified model, represents the level of green innovation at time for entity . corresponds to the internal demand factor, namely, the income level, at time . signifies the external demand factor, specifically the total exports, at time . stands for the Theil index, which represents the demand structure, at time . represents the vector of control variables. represents the individual fixed effects, capturing unobserved heterogeneity across entities. represents the time fixed effects, accounting for common time-related effects. symbolizes the independently and identically distributed random disturbances, satisfying and . stands for the spatial weight matrix, representing spatial interactions or relationships between entities and .
In this study, two distinct spatial weight matrices were employed. Firstly, a spatial inverse distance matrix
was constructed using geographical distances as the criterion:
In this context, represents the straight-line distance between the geographical centers of two regions.
This study also drew inspiration from the research conducted by Li [
63] and established an economic matrix
with the following expression:
In the provided equation, refers to the spatial inverse distance matrix. is the average per capita gross domestic product (GDP) of region at the observed time. denotes the average total GDP during the observed period, and represents different time periods.
Both of the aforementioned spatial weight matrices have been normalized by rows to eliminate the influence of data dimensionality. This approach ensures that the spatial interactions and their effects are captured effectively while accounting for the economic dynamics of the regions under investigation.
Before conducting spatial panel regression analysis, it is necessary to perform the relevant statistical tests. The first test is to examine whether there is a spatial correlation among the primary variables across different regions. After conducting the test, it was confirmed that spatial econometric models are appropriate for regression in this study. The spatial Durbin model was identified as the optimal choice.
Appendix A has a detailed presentation of the test. As in the research conducted by LeSage and Pace [
64], when the spatial term coefficient ρ in the model estimation results is not zero, it indicates the presence of spatial interactions between neighboring regions. Using only regression coefficients to explain spatial regression results can lead to biases. The parameter estimates of a spatial Durbin model do not directly reflect the true effects of direct effects and spatial spillover effects. The results need to be decomposed into direct effects, indirect effects, and total effects [
64]. The study presents the effects of local independent factors on local green innovation through their direct impacts. Spatial spillover effects, commonly known as indirect impacts, result from independent factors in neighboring regions influencing local green innovation. The combined value of direct and indirect impacts is referred to as total effects. Since those objects of spatial spillover effects are within China’s borders, the discussion of foreign demand factors, represented by total exports, is not included in the subsequent spatial econometric analysis. The results of the spatial econometric model are presented in
Table 7.
In terms of direct effects, the coefficients for income level and export volume are significantly positive, while the income gap coefficient is significantly negative. This implies that local green innovation is greatly benefited by the rise in domestic demand, which is mostly reflected in income levels. This outcome adheres to the idea of “demand-driven innovation”, which holds that demand is the main force behind green innovation. On the other hand, the widening income gap moderately inhibits local green innovation. This may be due to increased income inequality, leading to a reduction in the market size for green innovation products. Additionally, when comparing the direct effects obtained from the spatial panel model with the results of the basic regression that did not include spatial factors, the conclusions are consistent.
Regarding the indirect effects, the coefficient for income level is significantly negative, and the income gap coefficient is also significantly negative. This implies that changes in the demand levels of neighboring areas will exert a “siphoning effect” on local green innovation. Given the relatively high costs associated with green innovation, market demand levels are a critical factor considered by innovators. An increase in demand from neighboring areas attracts more green innovation elements, thereby promoting local green innovation. The impact of an irrational demand structure on green innovation also spills over from the local area to adjacent regions. An increase in the income gap in adjacent regions enhances the willingness of the high-income class to pay for innovative products, thereby increasing effective demand in adjacent areas. This encourages local green innovation to relocate to neighboring areas to gain more demand support, ultimately inhibiting local green innovation. It is worth noting that under the economic matrix condition, all coefficients are larger than when using a geographic distance matrix, indicating that spatial spillover is more pronounced among cities with similar levels of economic development. The findings above confirm Hypothesis H3a.
Previous research indicates that demand factors influencing green innovation could cause spatial spillover effects. Nonetheless, if local governments decide to implement regional protection strategies by artificially increasing the market entry barriers, it may lead to higher cross-regional circulation costs for various factors. Innovators may encounter challenges in carrying out innovative activities within a market demand environment that is not uniform, thereby hampering the market’s function of promoting “demand-induced innovation” [
65]. Currently, China’s economic landscape is characterized by the problem of factor market regulation by provincial and municipal governments, and market segmentation is a fact. This regulation, to some extent, helps local governments mobilize internal resources and advance short-term economic growth. Nevertheless, it does not support the sustainable development of innovation in the long term. Next, this paper will approach the issue objectively, introducing market segmentation variables and utilizing interaction terms for the empirical analysis of potential spatial moderating effects of market segmentation levels on the influence of demand factors on green innovation development.
We build upon the market segmentation calculation framework introduced by Parsley and Wei [
66]. It establishes a three-dimensional panel dataset encompassing time, regions, and product categories. To provide a more precise assessment of market segmentation across regions, the analysis focuses on consumer price indices (CPIs) for seven major commodity categories obtained from statistical yearbooks of various sample cities spanning the years 2008 to 2020. These categories encompass Food, Tobacco, and Alcohol; Clothing; Articles for Daily Use and Services; Health Care; Transportation and Communication; Residence; and Education, Culture, and Recreation. This methodological approach is designed to enhance the accuracy and reliability of our analysis, enabling a more comprehensive evaluation of market segmentation dynamics among different regions. See
Appendix B for details.
The regression results after introducing market segmentation are shown in
Table 8.
The results in
Table 8 indicate that upon introducing the interaction terms
and
, the indirect effect coefficients of market segmentation in relation to income level and income disparity are both negative and statistically significant. However, the direct effect of the interaction term is not statistically significant. This suggests that as market segmentation increases, the synergistic effect of income level on the neighboring regions’ green innovation becomes more pronounced, simultaneously intensifying the suppressive impact of an unreasonable demand structure on green innovation development in adjacent areas. This validates Hypothesis H3b. Thus, it follows that market segmentation has a detrimental effect on green innovation. This may be explained by the increasing degree of market segmentation, which raises barriers to entry for various geographic areas. As a result, expanding the scale of effective demand becomes challenging, ultimately hindering the inherent impetus for innovation. This situation is unfavorable for the progression of various forms of green innovation activities.
6. Discussion
This study examines the impact of demand factors on green innovation from three perspectives: domestic demand level, foreign demand level, and domestic demand structure. The empirical results based on Chinese city-level data confirm a positive association between increased demand levels and the development of green innovation, aligning with Hypotheses H1a and H1b proposed in this paper. Notably, it is not solely the magnitude of demand levels that matters; the research findings also reveal that an expanding income gap can impede green innovation. In contrast to previous studies that suggest negative implications of income inequality on technological innovation [
67,
68], this research underscores how income inequality may, in fact, act as an inhibitor of green innovation. This phenomenon may stem from an imbalance in demand structures, which reduces the market size for green innovation products, thereby impacting the emergence of green innovation.
Furthermore, the study’s results indicate that demand factors exert heterogeneous influences on green innovation across different regions and patent types, validating Hypotheses H2a and H2b. Horbach et al. [
11], in their research, categorized environmentally friendly innovations based on their varying environmental impacts and found differing levels of influence on different types of patents. This paper, employing data at the city level in China, in line with China’s patent categorization, distinguishes between invention patents and utility model patents within green innovation and reaches similar conclusions. Such heterogeneity offers valuable insights to policymakers, emphasizing the need for tailored, context-specific environmental policies that consider the requirements of different regions and patent types.
Finally, further analysis in this study demonstrates the presence of spatial spillover effects on the influence of demand factors on green innovation. This not only validates the last hypothesis, H3, but also aligns with the findings of Shao et al. [
5]. Additionally, leveraging the advantage of using city-level data, this paper delves into market segmentation and reveals that intensified market segmentation has a detrimental impact on the spatial spillover of the demand’s influence on green innovation. This provides an intriguing perspective for a comprehensive understanding of the spatial spillover effects of demand.
7. Conclusions
7.1. Conclusions
This study conducted empirical studies using a dataset that included 261 cities that were at the prefecture level or higher between 2008 and 2020 in China. The study utilized econometric techniques, including panel data models and spatial Durbin models, to examine the influence of demand determinants on green innovation. Additionally, spatial spillover effects and the moderating impact of market segmentation on demand-induced effects in the context of green innovation were investigated. The following are the study’s major findings: First, green innovation receives significant benefits from rising levels of domestic as well as overseas demand. Conversely, a widening income gap significantly inhibits the development of green innovation. Second, demand factors exhibit clear heterogeneity in their effects on green innovation, varying by patent type and regional characteristics. After introducing spatial factors, it was discovered that an elevation in demand levels in neighboring regions yields a detrimental spillover impact on local green innovation. Likewise, a rise in income inequality in neighboring regions also results in negative spatial spillover. The influence of demand considerations on green innovation is moderated by market segmentation. The more segmented the market is, the greater the “siphoning” effect when demand increases in nearby areas. Moreover, an unsuitable demand structure in neighboring regions exacerbates the hindering effect on local green innovation development. These results offer valuable perspectives on the varied impacts of demand-related factors on green innovation in China.
7.2. Implications
Based on the empirical findings presented in this paper, several policy implications can be derived. Firstly, our research findings underscore the importance of demand as a significant factor influencing the development of green innovation. Therefore, proactive measures should be taken to cultivate domestic and international demand markets. This includes continuously increasing disposable income for residents, enhancing their capacity to consume green innovation products, and leveraging domestic demand to drive green innovation. Additionally, it is essential to maintain a high level of international openness, actively introduce advanced international green technologies, and strengthen the protection of intellectual property rights related to international green technologies to stimulate foreign demand for green innovation. Furthermore, region-specific innovation policies tailored to local conditions should be established to promote green innovation. Our study reveals that demand factors have different impacts on green innovation in various regions. As a result, addressing disparities in green innovation among regions and exploring strategies for green innovation development in underdeveloped areas are crucial for enhancing green innovation efficiency. Lastly, expediting the development of unified factor markets is vital. This involves removing market fragmentation in domestic factor markets and establishing unified markets that allow for the free flow of factors. This, in turn, will significantly enhance the role of demand in driving green innovation development.
7.3. Limitations and Outlooks
Using econometric models, this research examines how demand factors affect green innovation. However, some limitations arising from the research conditions and perspectives require additional investigation. Firstly, we centered our study on Chinese prefecture-level cities, which provides a more detailed research scale. Nevertheless, given that there are about 300 such cities in China, we could not include all cities in our study due to data constraints. The unavailability of data limited the study’s ability to thoroughly examine some cities in the western regions. Improving data accessibility should be the main goal of future research. Using China as an illustrative example, this study also examined how demand factors influence green innovation in that country. However, due to the various economic, social, and innovative landscapes across different countries worldwide, further research could extend beyond China to offer valuable insights into driving green innovation on a big scale.