Study on the Promotional Effect and Mechanism of New Quality Productive Forces on Green Development

Abstract

The new quality productive forces have the potential to spur both the green transformation of the industrial structure and innovative advances in green technology, which will further strengthen the foundation for sustainable growth. This study analyzes panel data from 30 provinces between 2012 and 2022 to build an evaluation system for new quality productive forces and green development at the provincial level. The entropy weight TOPSIS approach is used to assign weights to each indicator. Methods including fixed effects, mediation effects, and spatial econometrics are used to examine the contribution of new quality productive forces to green development and its mediation mechanism. The study finds that: (1) New quality productive forces significantly promote green development, and the conclusion still holds after a robustness test using the instrumental variables method and excluding municipalities. (2) The new quality productive forces contribute significantly to green development by improving technology and optimizing industrial structure. (3) The new quality productive forces not only directly enhance the green development level of the region, but also positively influence the green development level of the neighboring regions through the spatial spillover effect. (4) The eastern and central regions are more affected by new productivity in terms of green development. Based on these, efforts should be made to develop new quality productive forces, increase technological research and investment, and promote the development of industrial structure to be more environmentally friendly and efficient to promote green development.

1. Introduction

As a core strategy for global sustainable development in the 21st century, green development is not only a key way to alleviate the pressure on resources and environment and promote the construction of ecological civilization, but it also an intrinsic requirement to promote economic transformation and upgrading and achieve high-quality development [1]. The connotation of green development is the increasing proportion of the green economy, emphasizing the symbiosis between economic, social, and natural systems and the diversification of development goals [2]. As the world’s largest developing country and a major carbon emitter, China has demonstrated its determination and positive actions in promoting green development by implementing a series of policies and measures, such as strengthening the construction of ecological civilization, promoting clean energy, and optimizing the industrial structure, thus contributing significantly to global green development.

Against the backdrop of global climate change and increasingly severe resource and environmental constraints [3], green development has become an important strategic choice for promoting sustainable economic and social development. The traditional productivity model, which relies on high-intensity resource consumption and environmental pollution, can no longer meet the dual demands of modern society for environmental protection and high-quality development. In this context, the rise of new quality productivity forces provides a new impetus and direction for green development. Compared with traditional productivity forces, new quality productivity forces are a profound transformation that abandons the old model of high energy consumption, high emissions, and low efficiency, and instead embraces a new production system centered on scientific and technological innovation and integrating cutting-edge technologies such as data, networking, and intelligence [4]. This transformation has fundamentally changed the path of productivity development, making production activities cleaner, more efficient, and more sustainable. In recent years, the development of new quality productivity forces in China has taken shape and achieved remarkable results. From the widespread use of clean energy to the flourishing of smart manufacturing, from the emergence of the digital economy to the rapid changes in biotechnology, these are all vivid depictions of the dynamic development of new quality productivity forces. Seres Group promotes the rapid development of the new energy automobile industry through technological innovation and intelligent manufacturing, with its range-extending 5.0 system achieving a thermal efficiency of 45% and an oil-to-electricity conversion efficiency as high as 3.65 kWh/L. As the world’s first 10,000-tonne photovoltaic (PV) green hydrogen demonstration project, SINOPEC’s Xinjiang Kuqa Green Hydrogen Project showcases the results of new quality productivity forces in the field of clean energy. The project’s PV generates nearly 600 million kWh of green electricity annually and produces 20,000 tonnes of green hydrogen, reducing CO₂ emissions by 485,000 tonnes per year, which is equivalent to planting 300,000 trees. All these changes have strongly promoted China’s green development process. However, the high-carbon energy structure and industrial structure are still the key factors restricting green development, and China’s green transformation road still needs to be sharpened. In this context, accelerating the development of new quality productivity forces is not only a fundamental change to the traditional mode of production but also a necessary way to promote green development and achieve high-quality development.

Based on this, what is the mechanism of the impact of new quality productive forces on green development? Are there regional differences in this impact? Are there spatial spillover effects? Thorough examinations of these matters will not only contribute to the theoretical development and enhancement of new high-quality productive forces, but they will also give decision-makers useful and efficient guidelines.

2. Literature Review

2.1. Study on New Quality Productivity Forces

Many useful research findings have been obtained thus far from the study of new quality productivity forces; one of the studies closely related to this paper focuses on the core connotations of new quality productivity forces, indicator evaluation, measurement, and influencing factors.

The majority of academics now concentrate mostly on the theoretical analysis of new quality productivity forces. Scholars have made a lot of theoretical discussions on the conceptual definition of new quality productive forces from the elements of productivity. Pu Qingping and Xiang Wang [5] suggest that new quality productive forces are composed of “high-quality” workers, “new medium” labor materials, and “new material quality” labor objects. Wei Chonghui [6] believes that the new quality productive forces stress the new qualitative state and highlight the transcendence of the “new” factors of production over the old factors of production. Zhang Lin and Pu Qingping [7] believe that it is a concept of total factor productivity that represents new technology, creates new value, and forms new industries. Gao Fan [8] points out that the new quality productive forces are based on the supply of given factors, relying on changes in factor organization, technology, and other changes to improve the efficiency of factor combinations, and in this way to form the incremental output of products or services.

In terms of evaluation indexes, the development level of new quality productivity forces is mostly assessed by constructing an index system, and this approach, from multiple dimensions, achieves a more comprehensive and in-depth measurement and assessment of new quality productivity forces. Scholars either construct the new quality productivity forces evaluation index system from four dimensions: new quality human resources, new quality science and technology, new quality industrial forms, and new quality production methods [9], or from four main indicators: live labor, materialized labor, hard technology, and soft technology [10], or based on the theory of the two elements of productivity [11], or from the three dimensions of digital laborers, digital labor materials, and digital labor objects [12].

In terms of measurement methods, scholars have either used the entropy method [13] or the projection-seeking clustering model based on a genetic algorithm [14] to analyze the development level of new quality productivity forces in China.

At the same time, some scholars have also studied and analyzed the influencing factors of new quality productivity forces, and it was found that digital financial inclusion [15], smart city construction [16], and artificial intelligence [17] all have a contributing effect on new quality productivity forces.

Although foreign studies have not directly focused on “new quality productivity forces”, they have extensively explored related fields such as economic momentum [18], innovation efficiency [19], and total factor productivity [20], which indirectly reveal the core elements and development trends of new quality productivity forces. In the context of economic dynamic transformation, the government pays more and more attention to the quality of economic development, the market mechanism can be fully activated, and the innovation efficiency of enterprises is significantly improved, which in turn promotes technological progress and ultimately promotes the realization of economic growth [21]. In addition, total factor productivity also has a significant role in promoting economic growth [22].

2.2. Study on Green Development

The research related to this paper focuses on four aspects: the connotation of green development, evaluation of indicators, measurement, and influencing factors.

Despite the differences in the interpretation of the definition of green development and its connotation, the international community widely agrees that it is a concrete practical way and feasible strategy to promote the goal of sustainable development and that green development contains the essential characteristics of promoting the harmonious coexistence and synergy of the goals of the environment, the economy, and the society in multiple dimensions [23,24].

In terms of evaluation indicators, a comprehensive evaluation system of green development is constructed either from three dimensions of economic development, social progress, and ecological civilization [25], or from four dimensions of resource conservation, environmental friendliness, ecological conservation, and quality efficiency [26], or from three dimensions of green ecology, green life, and green production [27]. Meanwhile, some scholars have also adopted green total factor productivity to represent green development [28].

In terms of measurement methods, it contains the use of the DPSIR model [29], input-output model [30], SBM model [31], and other methods.

In terms of the factors influencing green development, most scholars believe that the level of economic development, science and technology, urbanization level, and industrial structure have a positive influence on green development [32,33,34,35], while some scholars believe that industrial structure and environmental regulation are negative factors [36].

2.3. Study on the Relationship between New Quality Productivity Forces and Green Development

Currently, there are fewer studies combining both new quality productivity forces and green development, and most of them are theoretical studies. Liu and He [37] proposed that new quality productivity forces focus on the improvement of labor quality, the modernization and upgrading of labor materials, and the optimal matching of labor objects, aiming to lead the manufacturing industry to a new stage of intelligent and green development through the enhancement of the professional skills and innovation ability of the workers combined with the adoption of more advanced labor materials. Xu Zheng [38] points out that the development of new quality productivity forces releases green kinetic energy for the realization of carbon peaking and carbon neutrality. Zhang et al. [39] argue that studying how local government concerns affect green total factor productivity (GTFP) may have unintended implications for how new quality productivity forces are developed.

This paper empirically analyzes the role and influence mechanism of new quality productivity forces on green development based on the study of new quality productivity forces and green development level measurement in 30 provinces in China from 2012 to 2022. It further empirically analyzes the spatial effects of new quality productivity forces on green development, using the dynamic spatial Durbin model, and analyzes regional heterogeneity. Based on the limitations of the existing literature, this paper makes the following contributions: (1) Although there are abundant research results in the current academic field, most of them study new quality productivity forces and green development separately, and this paper incorporates new quality productivity forces and green development into the same framework. (2) The mechanism of the impact of new quality productivity forces on green development is explored from the perspectives of technological advancement and optimization of industrial structure, and the analysis of inter-regional heterogeneity is carried out by taking into account the factor endowments of different regions. (3) This paper fully considers the spatially relevant features and uses the spatial Durbin model to analyze the direct and indirect effects of the new quality productivity forces to enhance the level of green development in a relatively specific way.

3. Theoretical Analysis and Research Hypothesis

3.1. Principles of New Quality Productivity Forces Affecting Green Development

Under the guidance of the new development concept, new quality productivity forces have demonstrated the green characteristics of the efficient use of resources and the harmonious coexistence of the environment. In response to the challenges of regional green transformation, new quality productivity forces effectively promote the green development process of the region by promoting the green transformation of workers, labor objects, and labor materials, and realizing the green upgrading and qualitative change of the combination of these elements to generate green kinetic energy [40].

The new quality development of workers is significantly reflected in optimizing both supply and consumption. On the supply side, new quality productivity forces have become an important driving force, which motivates workers to actively explore and adopt green production methods to reduce environmental pollution, improve resource efficiency, and then produce products and services that are both environmentally friendly and sustainable [41]. These green supplies not only accurately meet the market’s urgent demand for environmentally friendly products, but also lead to a positive change in consumer attitudes. On the consumption side, workers, as consumers, practice green consumption behaviors and show their preference for environmentally friendly products [42], which further boosts the demand for the green market, forming a benign interaction between green supply and green consumption. New quality productivity forces are precisely through the precise application of measures on both sides of the supply and demand, to achieve the synergy of the two, thus comprehensively promoting the green practices of workers, and vigorously promoting green development.

The new quality productivity forces emphasize the central role of science and technology innovation in the production factor system, which has led to a far-reaching change in the object of labor [43]. This change is not only limited to the continuous improvement of traditional factors such as raw materials, energy, and intermediate products but also significantly extends to the field of new factors such as information, data, knowledge, and skills, giving rise to a new type of labor objects with the characteristics of intelligence, networking, greening, and digitization. With the advantages of high permeability, strong integration capacity, and wide spatial and temporal coverage, these new labor objects have achieved large-scale agglomeration and efficient use [44], providing a solid backing for green development.

The development of the new quality of labor means promotes the transformation of traditional manufacturing processes in the direction of greening and leads to the innovation and upgrading of equipment, which effectively breaks the constraints of high energy consumption and high pollution in the process of the economic growth of traditional industries [45], thus promoting green development.

In summary, the following hypotheses are presented in this paper:

Hypothesis 1. 

New quality productivity forces contribute significantly and positively to green development.

3.2. Mechanisms by Which New Quality Productivity Forces Influence Green Development

This paper examines the mechanism of new quality productivity forces for green development at two levels: on the one hand, the new quality productivity forces themselves emphasize the central role of innovation in the production process, promote cutting-edge exploration and breakthroughs in technology, significantly improve production efficiency, effectively reduce production costs, and promote green development; on the other hand, through the development of new quality productivity forces, they promote the transformation of industries to innovation-driven development, enhance the added value and market competitiveness of industries, realize the optimization and upgrading of industrial structure, and then provide solid industrial support for green development.

3.2.1. Technological Progress

The behavior and strategies of agents are decisive for economic and social evolution, with the behavior of technological innovation being particularly crucial. New quality productivity forces provide a strong impetus and inexhaustible kinetic energy for technological progress [46]. The development of new quality productivity forces is often closely linked to the emergence of new technologies, new processes, and new management models. The adoption of these innovative technologies and methods can greatly improve production efficiency, optimize product quality, and bring considerable economic benefits to enterprises, thus strongly promoting the continuous progress and development of technology [47]. In addition, new quality productivity forces show a cross-border integration characteristic, which prompts the original seemingly unrelated industrial fields can also explore new integration opportunities and new industrial forms [48]. This innovative practice of cross-border integration breaks the boundaries between traditional fields and opens up new directions and potential possibilities for technological progress.

Technological progress has a positive impact on the enhancement of the level of green development [49]. Technological progress is an important factor in development, and green development cannot be separated from the support of advanced technology. Green technology is undoubtedly an indispensable catalyst for promoting green economic growth, and its promotion and application have demonstrated significant contributions to mitigating global warming and reducing carbon emissions [50]. The significant sign of technological progress lies in the leap of technological efficiency, which plays a key role in effectively controlling carbon dioxide emissions and constitutes the core force for promoting green development [51]. In summary, this paper proposes the following hypothesis:

Hypothesis 2. 

New quality productivity forces enhance green development by promoting technological progress.

3.2.2. Optimization of Industrial Structure

With the introduction of new technologies, new modes, new products, and new industries, new quality productivity forces have given rise to new market demand and industrial opportunities, guided the convergence of resources to emerging fields, accelerated the decline of some traditional industries, and at the same time promoted the rapid growth of new industries [48]. The vigorous development of emerging science and technology industries also brings new vitality to the traditional industrial layout and promotes the development of the industrial structure to a higher level and wider field [52]. Emerging industries such as artificial intelligence, big data, cloud computing, and biotechnology are not only technologically advanced and effective but also show strong potential for sustainable development. New quality productivity forces can drive the optimization and upgrading of industrial structures [53].

Optimization of industrial structure implies the evolution from resource-intensive and highly polluting traditional industries to energy-efficient and low-polluting green industries. This transformation can significantly reduce the consumption of resources and the emission of pollutants, alleviate the pressure on the environment, and promote the improvement of the ecological environment. Li et al. [54] believe that the optimization of industrial structure is a key factor in improving the level of green development. Tian and Zhang [55] also believe that the optimization of industrial structures has an important role in promoting green development. The optimization of industrial structures can not only promote the development of green industry but also promote the progress of green consumption, green transport, green buildings, and other fields. The development of these fields forms a virtuous circle, further promoting the green development of the whole society. In summary, this paper proposes the following hypothesis:

Hypothesis 3. 

New quality productivity forces achieve green development by promoting the optimization and upgrading of industrial structures.

3.3. Spatial Effects of New Quality Productivity Forces on the Level of Green Development

The theory of spatial economics reveals that the spatial economy exhibits multiple or steady-state equilibria, with equilibrium models that incorporate labor migration, trade flows, productivity gains, and comfortable positive externalities in geospatial terms. In this theoretical framework, the lasting effects of temporary local shocks (or the unintended consequences of permanent local shocks) can be rationalized by first-nature differences, second-nature differences, frictions between multiple factors, or significant spatial spillovers. [56].

The distinctive characteristics of new quality productivity forces are mainly reflected in the fact that it takes innovation as its core driving force, in addition to its remarkable qualities of high technology, high performance, and integration. These unique attributes enable new quality productivity forces to not only have far-reaching impacts within their own regions, but also break through geographical boundaries, achieve extensive spatial spillover effects, and inject new vitality into green development [57]. On the one hand, against the backdrop of significant differences in regional development, the law of attraction has led to the strengthening of multidirectional imitation and learning mechanisms among regions, which in turn has given rise to the spontaneous diffusion of new quality productivity forces. In this process, factors such as production factors, scientific research and technology, and talent exchanges, which are closely related to green development, are accompanied by the wide dissemination of new quality productivity forces, and their overflow circulation potential and absorption and integration capacity can be activated and continuously improved, providing a strong impetus for the deepening of green development [58]. On the other hand, with the continued development of new quality productivity forces, the development dynamics of the regions have not only achieved a strong continuity and a continuous release of vitality but have also contributed to an increasing degree of interregional coordination and an elevated level of collaboration. This positive interaction between regions has further accelerated the pace of innovation of green factors of production in local and neighboring regions, ultimately promoting the overall improvement of the level of green development and achieving regional synergy and overall progress in green development. In summary, this paper puts forward the following hypotheses:

Hypothesis 4. 

New quality productivity forces have spatial spillover effects and can enhance the level of green development in neighboring regions.

4. Research Design

4.1. Variable Description

4.1.1. Dependent Variable

Existing research mainly focuses on quantifying the efficiency of green development from the perspective of the input–output ratio, and although this assessment method focuses on the optimal use of resources, it is often easy to ignore the multidimensional value of green development in production and life. Therefore, based on the practice of scholars such as Li Pingrui [59,60] and the availability of data, the article constructs a green development evaluation system from three perspectives, green production, green life, and green ecology, as shown in

Table 1. Green development level evaluation index system.

Level 1 Indicator	Level 2 Indicator	Level 3 Indicator	Measurements and Units	Causality
Level of green development	Green production	Share of tertiary sector value-added	Tertiary value added/GDP (%)	+
		Electricity consumption per unit of GDP	Electricity consumption/GDP (%)	−
		Industrial water consumption	Industrial water consumption (billion cubic meters)	−
		Number of patents granted for green inventions	Number of patents granted for green inventions (item)	+
	Green Living	Per capita disposable income	Per capita disposable income (CNY)	+
		Public transport vehicles per 10,000 population	Public transport vehicles per 10,000 population (standard table)	+
		Daily domestic water consumption per capita	[(Water consumption for residential households + water consumption for public services + water consumption for domestic use from free water supply)/population using water]/number of calendar days in the reporting period × 1000 L (liter)	−
		NOx emissions per capita	NOx emissions/total population (tonnes/person)	−
	Green ecology	Non-hazardous domestic waste disposal rate	Non-hazardous domestic waste disposal rate (%)	+
		Parkland per capita	Parkland per capita (square meters/person)	+
		Greening coverage in built-up areas	Area covered by greenery in built-up areas/area of built-up areas (%)	+

.

Considering the continuous progress of multi-criteria decision-making methods, when choosing the weight calculation method, according to Wiȩckowski and Sałabun’s research [61], this paper comprehensively used to carry out calculations and analyses, and found that the entropy weight TOPSIS method is more suitable for this paper’s research background and needs, and so finally chose to use the entropy weight TOPSIS method. The following are the detailed steps of the entropy weight TOPSIS method:

The entropy weight method is first used to measure the weight of indicators. Subsequently, the TOPSIS method was further used to measure the comprehensive score based on the indicator weights. The method is based on the ranking of the evaluation objects based on their proximity to positive and negative ideal values. Specifically, by measuring the positive and negative ideal values of the indicators, the difference between the distances of the positive and negative ideal values is used to calculate the relative posting progress, and the ranking is based on the advantages and disadvantages of the posting progress [62]. The steps are as follows:

In the first step, the original data matrix of n indicators in m provinces (districts and cities) at moment t is established as X = (x_ij)_m×n (i = 1, 2, ..., m; j = 1, 2, ..., n); and then the evaluation indicators are standardized, and the dimensionless standardization is carried out for the positive indicators and the negative indicators as follows.

$$x_{ij}^{\prime }(t)={\displaystyle \frac{x_{ij}(t)-min\left\{x_{ij}(t)\right\}}{max\left\{x_{ij(t)}\right\}-min\left\{x_{ij}(t)\right\}}}(+)$$

(1)

$$x_{ij}^{\prime }(t)={\displaystyle \frac{max\left\{x_{ij}\left(t\right)\right\}-x_{ij}\left(t\right)}{max\left\{x_{ij\left(t\right)}\right\}-min\left\{x_{ij\left(t\right)}\right\}}}(-)$$

(2)

where t = 1, 2, ..., T.

In the second step, the information entropy and the coefficient of variation are calculated with the formulae, respectively:

$$e_j=-\mathrm{k}{{\sum }_{t=1}^T\sum }_{i=1}^m{\displaystyle \frac{x_{ij}^{\prime }}{{{\sum }_{t=1}^T\sum }_{i=1}^m{x}_{ij}^{\prime }}}\times ln\left({\displaystyle \frac{x_{ij}^{\prime }}{{{\sum }_{t=1}^T\sum }_{i=1}^m{x}_{ij}^{\prime }}}\right)$$

(3)

$$d_j=1-e_j$$

(4)

where k is a constant, k = (ln m)⁻¹.

In the third step, the weights of the indicators were calculated, that is, the coefficient of variation is normalized:

$$w_j={\displaystyle \frac{d_j}{{\sum }_{i=1}^n{d}_j}}$$

(5)

In the fourth step, based on the weights of the indicators, the normalized decision matrix P = [p_ij]_(m×n) is constructed, and the formula is calculated:

$$p_{ij}=x_{ij}^{\prime }·w_j;p_i={\sum }_{j=1}^n{p}_{ij}$$

(6)

In the fifth step, it is assumed that the maximum value of indicator j among all evaluation objects is $p_j^{+}$ and the minimum value is $p_j^{-}$. The Euclidean distance between the evaluation object indicator and the maximum value is:

$$D_i^{+}=\sqrt{{\sum }_{j=1}^m{\left(p_{ij}-p_j^{+}\right)}^2}$$

(7)

$$D_i^{-}=\sqrt{{\sum }_{j=1}^m{\left(p_{ij}-p_j^{-}\right)}^2}$$

(8)

In the sixth step, the relative advection rate (R_i) is calculated:

$$R_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(9)

4.1.2. Core Explanatory Variable

By measuring the level of new quality productivity forces, it is possible to deeply analyze the deficiencies in the development process from an objective and multi-dimensional perspective. Considering the availability of research data, this paper explores the construction of a comprehensive evaluation index system based on the approach of Jue Wang and Rongji Wang [63], which contains three core dimensions of laborers, objects of labor, and means of production, as shown in

Table 2. Indicator system for evaluating the level of development of new quality productive forces.

Level 1 Indicator	Level 2 Indicator	Level 3 Indicator	Level 4 Indicator	Measurements and Units	Causality
Level of development of new quality productivity	Laborer	Worker skills	Educational attainment per capita	Average years of schooling per capita (year)	+
			Human capital structure of the labor force	Educational attainment of the labor force was classified into five levels and measured using the vectorial angle method	+
			Structure of students enrolled in higher education institutions	Number of university students/total population (%)	+
		Labor productivity	Per capita output	GDP/total population (CNY)	+
			Per capita income	Average wage of employed workers (CNY)	+
		Labor awareness	Share of employees in the tertiary sector	Tertiary employment/total employment (%)	+
			Entrepreneurial activity	Number of new start-ups per 100 persons (units/100 persons)	+
	Object of labor	Technology industry	Technology market turnover	Technology market turnover (CNY 10,000)	+
			Number of robots	Number of robots/total population (units/10,000 persons)	+
		Ecological environment	Forest cover	Forest area/total land area (%)	+
			Environmental protection efforts	Expenditure on environmental protection/government expenditure on public finance (%)	+
			Pollutant emissions	(SO2 emissions + wastewater emissions + general industrial solid waste generation)/GDP (tonnes/yuan)	−
			Industrial waste management	Industrial wastewater and gas treatment facilities (sets)	+
			Utilization of waste materials	Comprehensive utilization/generation of industrial solid waste (%)	+
	Means of production	Material means of production	Traditional infrastructure	Road mileage, railway mileage (10,000 km)	+
			Digital infrastructure	Length of fiber(kilometer), number of Internet broadband access ports per capita (per person)	+
			Overall energy consumption	Energy consumption/GDP (%)	-
			Renewable energy consumption	Renewable energy electricity consumption/social electricity consumption (%)	+
		Intangible means of production	Patents per capita	Number of patents granted/total population(piece/person)	+
			R&D inputs	R&D expenditure/GDP (%)	+
			Digital economy	Digital economy index	+
			Enterprise digitization	Enterprise digitization levels	+

. The index system of new quality productivity forces covers a wide range of subdivided indexes of various types and units, and the differences between these indexes in terms of units, scales, and orders of magnitude may have interfered with the assignment process of the comprehensive evaluation. Given this, to ensure the validity of the results, the indicators need to be treated with dimensionless quantification. This paper adopts the extreme value method to standardize the indicator data, ensuring that the processed data values fall within the interval of [0, 1], to achieve data standardization and unification. This paper adopts the entropy weight method to determine the weights of the indicators of each dimension of new quality productivity forces.

4.1.3. Mediator Variable

Technological progress (Inn) uses new product sales revenue of industrial enterprises above designated size as a proxy variable; industrial structure optimization (ISO) is represented by the following equation:

$$\mathit{ISO}={\sum }_{j=1}^3{\displaystyle \frac{R_{ijt}}{R_{it}}}\times {\displaystyle \frac{R_{ijt}}{Y_{ijt}}},j=1,2,3$$

(10)

In Equation (10), $R_{\mathit{ijt}}$ reflects the value added of the output of industry j in province i in period t, $R_{\mathit{it}}$ reflects the regional GDP of province i in period t, and $Y_{\mathit{ijt}}$ reflects the number of employed persons in industry j in province i in period t. Finally, the data for both variables are standardized.

4.1.4. Control Variable

There are more factors affecting green development. To reduce the estimation bias caused by the omitted variables and more accurately assess the impact of new quality productivity on green development, this paper draws on existing studies to introduce relevant variables [64,65] and selects the following indexes as control variables: (1) Social consumption level (SCL), total retail sales of social consumer goods/GDP. The level of consumption in a society reflects the dynamism of the economy, and different levels of consumption may affect the demand for green products and services and thus green development. (2) Environmental regulation (ENR), industrial pollution control completed investment/GDP. This ratio is usually regarded as a measure of the importance attached to environmental regulation; a higher ratio indicates that the region has invested more in environmental regulation, thus reflecting a correspondingly higher intensity of environmental regulation. The intensity of environmental regulation directly affects the effectiveness of green development [66]. (3) Level of human capital (LHC), expressed as the logarithm of the number of tertiary institutions. Improvement in the level of human capital has a catalytic effect on green development [67]. (4) Population density (DP) is measured using the ratio of the number of urban population to urban area, which is log-transformed. Population density reflects the degree of urbanization, and high-density areas may face greater environmental pressures and resource constraints, thus affecting green development. (5) Government size (GS), measured by local government fiscal expenditures and log-transformed. The size of government reflects its capacity in resource allocation and public service delivery, and government input and support are critical to green development. (6) Level of infrastructure development (LIC), measured by road area per capita. Good infrastructure reduces resource consumption and environmental pollution and promotes sustainable development.

4.2. Data Sources and Descriptive Statistics

Due to the availability of data, the study period of this paper is set as 2012–2022, and the spatial scale is 30 provinces in China (excluding Hong Kong, Macao, Taiwan, and Tibet). The sources of data include the National Bureau of Statistics (NBS), China Public Policy and Green Development Research Database (CPPGD), China Industrial Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, and provincial statistical yearbooks. Given the small amount of missing raw data, interpolation was used to reduce sample loss (

Table 3. Descriptive statistics of variables.

			(1)	(2)	(3)	(4)	(5)
Variable Name	Variable Symbol	Measurements and Units	Sample Size	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Values
Level of green development	Grede	Establishment of a system of indicators, as shown in Table 1	330	0.350	0.109	0.186	0.800
New qualitative productivity levels	Nqf	Establishment of a system of indicators, as shown in Table 2	330	0.135	0.0629	0.0479	0.485
Social consumption level	SCL	Total retail sales of consumer goods/GDP (%)	330	0.393	0.0656	0.180	0.610
Environmental regulation	ENR	Completed investment in industrial pollution control/GDP (%)	330	0.00105	0.00121	7.57 × 10−6	0.0110
Level of human capital	LHC	Logarithm of the number of higher education institutions	330	4.318	0.629	2.197	5.124
Population density	DP	Ratio of urban population size to urban area, in logarithms	330	8.961	0.608	7.194	10.13
Size of government	GS	Local government fiscal expenditures, in logarithms	330	8.483	0.583	6.762	9.827
Level of infrastructure development	LIC	Road area per capita (square meters)	330	16.81	5.042	4.080	28
Technological progress	Inn	Revenue from sales of new products by industrial enterprises above designated size, standardized treatment	330	0.129	0.182	0.0001	1.000
Optimization of industrial structure	ISO	See Equation (10), standardized treatment	330	0.250	0.182	0.0001	1.000

).

4.3. Model Setting

4.3.1. Baseline Measurement Model

To verify whether the growth of new quality productivity forces is favorable to green development, this paper adopts a two-way fixed effect model. The model is set up as follows:

$${Grede}_{it}={\alpha }_0+{\alpha }_1{Nqf}_{it}+{\alpha }_2{Con}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(11)

where: ${\mathit{Grede}}_{\mathit{it}}$ denotes the green development level of province i in period t; ${\mathit{Nqf}}_{\mathit{it}}$ denotes the new quality productivity forces level of province i in period t; ${\mathit{Con}}_{\mathit{it}}$ denotes a series of control variables, including social consumption level (SCL), environmental regulation (ENR), level of human capital (LHC), population density (DP), government size (GS), and level of infrastructural development (LIC); ${\alpha }_0$, ${\alpha }_1$, and ${\alpha }_2$ denote the parameters to be estimated; ${\mu }_i$ denotes the individual fixed effects; and $v_t$ denotes the time fixed effects; ${\epsilon }_{\mathit{it}}$ denotes the random disturbance term.

4.3.2. Mediation Models

As can be seen from the results of established academic research, previous analyses of mechanisms have mostly followed the methodology of Wen Zhonglin et al. [68]. To avoid the potential limitations of this approach, this paper is based on the benchmark regression model and draws on the research method of Jiang Ting [69] to empirically test the mechanism of how new quality productivity forces affect green development. The following mediation model is constructed:

$$M_{it}={\beta }_0+{\beta }_1{Nqf}_{it}+{\beta }_2{Con}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(12)

Equation (12) shows the effect of new quality productivity forces on the mediating variables, M denotes the mediating variables, namely technological progress (Inn), industrial structure optimization (ISO); ${\beta }_0$, ${\beta }_1$, ${\beta }_2$ denote the parameters to be estimated. If ${\beta }_1$ is significant, it means that new quality productivity forces have an impact on green development through the mediating variables.

4.3.3. Spatial Effects Model

Considering the possible regional spatial spillover effects, the general panel model cannot capture the inter-district spatial linkages and impacts. After testing, this paper constructs a fixed spatial Durbin model. The specific form is as follows:

$${Grede}_{it}={\alpha }_3+\rho W{Grede}_{it}+{\beta }_X{Nqf}_{it}+{\rho }_{X}W{Nqf}_{it}+{\beta }_c{Con}_{it}{+\rho }_c{WCon}_{it}+{\mu }_i+v_i+{\epsilon }_{it}$$

(13)

where, ${\mathit{Grede}}_{\mathit{it}}$ is the green development level of the province (municipality directly under the central government) in year t, $W{\mathit{Grede}}_{\mathit{it}}$, $W{\mathit{Nqf}}_{\mathit{it}}$, and ${\mathit{WCon}}_{\mathit{it}}$ are the spatial lag terms of the explanatory variables, explanatory variables, and control variables, respectively, W is the spatial weight matrix, and $\rho$ is the spatial autoregressive coefficient. ${\mathit{Con}}_{\mathit{it}}$ is the control variable, ${\beta }_X$ is the coefficient corresponding to the core explanatory variables, and ${\rho }_X$ is the influence of the region on the neighborhood. ${\mathit{Con}}_{\mathit{it}}$ is the control variable. ${\beta }_X$ is the coefficient corresponding to the core explanatory variable, and ${\rho }_X$ is the influence of the region by its neighbors. ${\mu }_i$ is the spatial fixed effect, $v_i$ is the time fixed effect, and ${\epsilon }_{\mathit{it}}$ is the error term. This paper focuses on exploring spatial relationships by constructing an economic distance matrix W. The spatial weight matrices used in the empirical evidence are normalized.

5. Empirical Results and Analyses

5.1. Benchmark Regression Results

Before doing any regression, multicollinearity was tested. The results showed that there was no significant multicollinearity between the variables because neither of the core explanatory variables had a VIF value greater than 5 nor did the other variables have a value greater than 10 (

Table 4. Multiple covariance test.

Variant	VIF
Nqf	2.23
SCL	1.32
ENR	1.29
LHC	5.18
DP	1.33
GS	6.92
LIC	1.12
Mean VIF	2.77

).

Table 5. Benchmark regression results.

Variable	(1)	(2)
	Grede	Grede
Nqf	0.266 ***	0.208 ***
	(0.028)	(0.030)
SCL		0.012
		(0.018)
ENR		2.278 **
		(1.039)
LHC		−0.107 ***
		(0.018)
DP		0.013 **
		(0.005)
GS		0.064 ***
		(0.011)
LIC		0.001 **
		(0.001)
_cons	0.236 ***	0.033
	(0.004)	(0.124)
year	YES	YES
province	YES	YES
N	330.000	330.000
R2	0.942	0.955

Note: ***, and ** indicate that the significance test at the 1 percent, and 5 percent levels were passed, respectively. No significant heteroscedasticity problems were found with the data in this paper, and the choice was made to use ordinary standard errors, as in the following table.

reports the estimation results of new quality productivity forces on green development. Column (1) is the regression result without control variables and column (2) is the regression result with control variables added, it is observed that the value of R2 changes from 0.942 to 0.955, which indicates that the explanatory power of the model gradually increases. The coefficients of the core explanatory variables in columns (1) and (2) are positive and significant, which indicates that the development of new quality productivity forces promotes the improvement of the level of green development, and this empirical result initially confirms the validity of Hypothesis 1. Meanwhile, the statistical results of the main control variables are also basically consistent with the findings of existing studies. Among them, the coefficient of the level of human capital (LHC) is negative, which may be due to the insufficiently close cooperation between colleges universities, and enterprises, resulting in the inability of educational resources to be transformed into actual productivity.

5.2. Robustness Check

5.2.1. Implementation of Data Shrinkage, Lagging of Explanatory Variables by One Period, and Exclusion of Municipalities from the Data

To further consolidate the robustness and validity of the benchmark regression results, this paper adopts the methods of eliminating extreme values and lagging the explanatory variables by one period to carry out the robustness test. (1) Considering that extreme values and outliers may have an impact on the regression results, the two main variables of new quality productivity forces and green development are subjected to the shrinking of the upper and lower 1% and regressed again on this basis, and the results of the regression are shown in column (1) of

Table 6. Robustness test results.

Variable	(1)	(2)	(3)
	Data Reduction Processing	Explanatory Variables Lagged by One Period	Excluding Municipalities
Nqf	0.236 ***	0.191 ***	0.203 ***
	(0.034)	(0.035)	(0.030)
SCL	0.012	0.019	0.026
	(0.018)	(0.019)	(0.018)
ENR	2.471 **	2.403 **	2.280 **
	(1.036)	(1.073)	(0.979)
LHC	−0.101 ***	−0.094 ***	−0.089 ***
	(0.018)	(0.019)	(0.018)
DP	0.016 ***	0.010 *	0.015 ***
	(0.005)	(0.006)	(0.005)
GS	0.064 ***	0.060 ***	0.056 ***
	(0.011)	(0.012)	(0.013)
LIC	0.001 **	0.001 *	0.002 ***
	(0.001)	(0.001)	(0.001)
_cons	−0.015	0.056	−0.107
	(0.124)	(0.128)	(0.133)
year	YES	YES	YES
province	YES	YES	YES
N	330.000	330.000	286.000
R2	0.954	0.951	0.961

Note: ***, **, and * indicate that the significance test at the 1 percent, 5 percent, and 10 percent levels were passed, respectively.

. It can be found that the estimated value, direction, and significance of new quality productivity forces remain relatively stable, verifying that the conclusions drawn from the benchmark regression model are still valid. (2) Given that there is a certain time delay in the impact of the development of new quality productivity forces on regional green development, the explanatory variables are treated with one period lag. The regression results are shown in column (2) of Table 6. (3) Considering that municipalities usually enjoy more policy favors, these policy factors may have an important impact on the development of new quality productivity forces; based on this, the regression is executed after removing the data of municipalities. The results are detailed in column (3) of Table 6. It can be seen that the impact coefficient of new quality productivity forces is still significantly positive, confirming that the benchmark regression results are still robust. The above results show that the coefficients of new quality productivity forces are all significant at the 1 percent level, that is, the development of new quality productivity forces can promote green development, which verifies the reliability of the benchmark regression.

5.2.2. Instrumental Variable

To deeply verify the robustness of the results, the instrumental variable method is applied to deal with the possible endogeneity problem. In this paper, we draw on the approach of Wang Yuhao and Ma Yeqing [70] to construct an interaction term as an instrumental variable to measure new quality productivity forces. In this paper, the interaction term (NqfI) between the number of effective invention patents of industrial enterprises above large scale in 2004 and Nqf with one period lag is chosen as an instrumental variable mainly based on the following considerations: on the one hand, the number of effective invention patents of industrial enterprises above large scale is directly related to and promotes the development of new quality productivity forces, which provides them with the impetus of innovation and quality assurance; on the other hand, the 2004 number of effective invention patents of industrial enterprises above large scale is the most important indicator of the endogeneity problem. On the other hand, the number of effective invention patents of industrial enterprises above scale in 2004 is one of the earlier and comprehensive data available to researchers. Although the number of effective invention patents of industrial enterprises above scale in 2004 represents the innovation level at that time, it does not form a direct and significant causal link or driving effect with the current concept and practice of green development. Columns (1) and (2) in

Table 7. Regression results of instrumental variable method.

Variant	(1)	(2)
NqfI	0.025 *** (8.70)
Nqf		0.222 ** (2.31)
Kleibergen–Paap rk LM statistic	3.542 (0.0598)
Kleibergen–Paap rk Wald F-statistic	75.74 {16.38}
controls	YES	YES
year	YES	YES
Province	YES	YES
N	330.000	330.000

Note: ***, and ** indicate that the significance test at the 1 percent, and 5 percent levels were passed, respectively. Numbers within {} are critical values for the 10% level of the weak identification test.

show the results of the one-stage and two-stage regressions in the 2SLS, respectively, and new quality productivity forces still significantly contribute to green development after taking the instrumental variable of NqfI into account. The LM statistic of Kleibergen–Paap rk significantly rejects the hypothesis of under-identification; and the Kleibergen–Paap rk Wald F statistic has a value of 75.74, which exceeds the critical value of 16.38 at the 10% significance level of the Stock–Yogo weak identification test, effectively ruling out the weak instrumental variable problem. After excluding the possible endogeneity problem, the results still show that new quality productivity forces have a significant positive impact on green development.

5.3. Impact Mechanism Analysis

The results of the benchmark regression confirm that the development of new quality productivity forces has a positive impact on green growth, and this paper expands the study of the transmission mechanism of new quality productivity forces for green development based on the theoretical assumptions of the previous paper.

5.3.1. Mechanism Variable: Technological Progress

Column (1) of

Table 8. Results of the test of the mechanism of the impact of new quality productive forces on green development.

Variant	(1)	(2)
	Technological Progress	Optimization of Industrial Structure
Nqf	1.324 *** (12.44)	0.300 ** (2.53)
_cons	−2.236 *** (−5.13)	1.400 *** (2.88)
controls	YES	YES
province	YES	YES
year	YES	YES
N	330.000	330.000
R2	0.689	0.824

Note: ***, and ** indicate that the significance test at the 1 percent, and 5 percent levels were passed, respectively.

shows that the estimated coefficient of new quality productivity forces is significantly positive, indicating that new quality productivity forces can contribute to technological progress. The new quality productivity forces themselves have high-tech, high-efficiency, and high-quality characteristics, which require continuous scientific research and technological breakthroughs to meet the needs of their development. This demand has driven research institutions, universities, and enterprises to increase investment in scientific research and carry out R&D and innovation in cutting-edge technologies, thus giving rise to new technological achievements and raising the level of technological innovation. The application of technological progress achievements reduces environmental pollution, thus promoting green development. The above empirical results verify the reasonableness of Hypothesis 2.

5.3.2. Mechanism Variable: Optimization of Industrial Structure

The estimated coefficient of new quality productivity forces in column (2) of Table 8 is significantly positive at the 5 percent level, which means that new quality productivity forces can promote the optimization of the industrial structure. New quality productivity forces are increasingly becoming the core driving force leading high-quality development, comprehensively and systematically promoting innovation in the field of science and technology, transforming and optimizing traditional industries, cultivating and expanding new industries, and promoting the in-depth transformation and upgrading of industrial structure. The upgrading of industrial structure helps to improve the efficiency of resource use, promote the development of technology-intensive industries led by cutting-edge technology, reduce energy consumption per unit of product, and further promote green development [71]. This empirical result verifies Hypothesis 3.

5.4. Spatial Effects Analysis

5.4.1. Spatial Correlation Test

The global correlation of regions is usually measured by global Moran’s I. In this paper, the economic distance weight matrix is used to test the global Moran’s I index of China’s 30 provinces from 2012 to 2022, and the detailed results are shown in

Table 9. Global spatial correlation of green development levels.

Vintages	Global Moran’s I	Z	p-Value
2012	0.274	3.753	0.000
2013	0.290	3.927	0.000
2014	0.302	4.139	0.000
2015	0.319	4.286	0.000
2016	0.336	4.419	0.000
2017	0.345	4.472	0.000
2018	0.313	4.128	0.000
2019	0.312	4.080	0.000
2020	0.319	4.132	0.000
2021	0.329	4.179	0.000
2022	0.291	3.715	0.000

. The Moran’s I index floats slightly above and below 0.313 but keeps an upward development trend in general, which are all greater than 0 and significant at 1% level, indicating that there exists a certain degree of spatial dependence within the region and the linkage is strengthening so that the region can be studied in depth using the spatial measurement model.

The spatial clustering is analyzed in depth using the Moran scatterplot, in which the names of provinces are replaced by abbreviations in the following Moran scatterplot, see Figure 1. In this case, quadrants one and three represent high–high agglomeration and low–low agglomeration, respectively, and quadrants two and four represent low–high agglomeration and high–low agglomeration, respectively. In terms of the national level, in the green development test, there were 6 and 19 provinces in the first and third quadrants, respectively, in 2012, accounting for 83.33%; there were 9 and 20 provinces in the first and third quadrants, respectively, in 2017, accounting for 96.67%; and there were 6 and 19 provinces in the first and third quadrants, respectively, in 2022, accounting for 83.33%. The third quadrant (low–low combo) and the fourth quadrant (high-complexity) are the most important provinces in China, and this trend shows that there is a kind of “convergence club” among the provinces, where similar development patterns or trends exist in the promotion of green development.

5.4.2. Model Selection Tests

To select the appropriate spatial econometric model, a series of selection tests were conducted in this paper. Based on the data analysis in

Table 10. Statistics related to model selection.

Test	Statistic	p-Value
LM Error	10.482	0.001
LM Error (Robust)	13.618	0.000
LM Lag	72.986	0.000
LM Lag (Robust)	76.122	0.000
Wald-SDM-SAR	24.80	0.001
Wald-SDM-SEM	32.66	0.000
LR-SDM-SAR	25.74	0.001
LR-SDM-SEM	32.90	0.000
Hausman	37.48	0.001

, the Lagrange multiplier test (LM) rejects the original hypothesis, indicating that all three spatial econometric models are feasible. Further, the likelihood ratio test (LR) and the Wald test (Wald) informed that the spatial Durbin model does not degenerate into a spatial error model or a spatial lag model. Given this, this paper chooses the spatial Durbin model based on the consideration of the applicability of the model, which can not only reveal the spatial correlation of the explanatory variables among regions but also reflect how the explanatory variables are affected by the spatial effects of the explanatory variables in other regions. The results of the Hausman test (Hausman) concluded that the model should choose fixed effects. Combining the above analyses, the spatial Durbin model with fixed effects was finally chosen.

5.4.3. Analysis of Spatial Effects Results

The specific regression results are shown in

Table 11. Spatial Durbin model (SDM) estimation results with double fixed effects.

Variant	Regression Coefficient	Z-Value
Nqf	0.141 ***	4.92
W × Nqf	0.149 *	1.70
$\rho$	0.283 ***	3.13
controls	YES	YES
province	YES	YES
year	YES	YES
N	330.000	330.000
R2	0.946

Note: ***, and * indicate that the significance test at the 1 percent, and 10 percent levels were passed, respectively.

. Since the coefficients of the explanatory variables in the regression results of the spatial econometric model do not directly map the extent of their impact on the explanatory variables, the analyses in this paper will focus on the positivity and negativity of the coefficients as a way of exploring the trend of their impact. This paper focuses only on the impact of the core explanatory variable: new quality productivity forces.

As can be seen from Table 11, the autoregressive coefficient ρ of the explanatory variable green development is 0.283, which passes the 1% significance test, and the validity of the spatial Durbin model (SDM) constructed in this study is verified. It also reveals the significant spatial spillover effect in the green development of China’s provinces. This means that the improvement of the green development level of a certain region has a positive impact on the green development level of the neighboring regions. The regression coefficient of the core explanatory variable, new quality productivity forces (Nqf), is positive and significant at the 1 percent level, indicating that the increase in the level of green development can be promoted by improving the level of new quality productivity forces.

To analyze the extent of the impact of new quality productivity forces on green development even further and to provide further analysis of spatial spillover effects, this paper refers to the research method of Lesage and Pace [72] to decompose the total effect of explanatory variables into direct and indirect effects, where the direct effect focuses on the extent of the impact of the local explanatory variables on the local explanatory variables, and the indirect effects reveal the potential impact of the explanatory variables in the region on the potential impact of the explanatory variables in the surrounding area. See

Table 12. Decomposition of spatial effects.

Variant	Direct Effect		Indirect Effect		Aggregate Effect
	Ratio	Z-Value	Ratio	Z-Value	Ratio	Z-Value
Nqf	0.152 ***	5.21	0.248 **	2.30	0.400 ***	3.60
controls	YES
province	YES
year	YES
N	330.000
R2	0.946

Note: ***, and ** indicate that the significance test at the 1 percent, and 5 percent levels were passed, respectively.

for details.

In terms of the direct effect of new quality productivity forces, its impact on the level of local green development is 0.152, that is, under the assumption that other variables remain unchanged, every 1 percent increase in the level of new quality productivity forces will push the level of local green development up by 15.2 percent. It is significant at the 1 percent level, proving that the increase in the level of new quality productivity forces has a significant positive driving effect on the level of local green development. In terms of the indirect effect of new quality productivity forces, its impact on the green development level of neighboring areas is 0.248 and significant at the 5% level, indicating that economic development also has a significant impact on the growth of green development level in neighboring areas. In terms of the total effect of new quality productivity forces, its overall effect on the level of green development in local and neighboring areas is 0.400 and significant at the 1% level, indicating that new quality productivity forces can have a significant contribution to green development. This also verifies Hypothesis 4. The spatial spillover effect is mainly reflected in the significant promotion of new quality productivity forces in the region on the green development of neighboring regions, which mainly stems from its strong spillover and demonstration effects. As the new quality productivity forces in the region continue to grow, their pulling effect on the relevant industries in the neighboring regions is also increasing. This pulling effect not only promotes the green transformation of the relevant industries in neighboring regions, but also promotes the green upgrading of the entire industrial chain, forming interregional synergies for green development, and also promotes the green development of the entire region.

5.5. Heterogeneity Analysis

On this basis, the provinces of the country were also divided into three parts: east, center, and west, and the degree of influence of new quality productivity forces development on green development in different regions was examined separately. The results are shown in

Table 13. Impact of new quality productive forces on the level of green development in different regions.

Variant	(1)	(2)	(3)
	Eastern Part	Central Section	Western Part
Nqf	0.153 ***	0.530 ***	0.156
	(3.93)	(3.70)	(1.38)
_cons	1.356 ***	0.102	−0.131
	(5.14)	(0.34)	(−0.69)
controls	YES	YES	YES
year	YES	YES	YES
province	YES	YES	YES
N	121.000	88.000	121.000
R2	0.973	0.976	0.966

Note: *** indicates that the significance test at the 1 percent level was passed, respectively.

. It can be found that its promotion effect is more significant in the east and center, and its promotion effect in the west is not significant.

The reason behind this lies, first of all, in the fact that the eastern region, as the most economically prosperous region in China and a leader in scientific and technological innovation strength, has an inherent advantage in developing new quality productivity forces. The industrial structure here is relatively optimized, with the rapid development of high-tech industries and modern services, and relatively low dependence on resources and the environment. Therefore, the development of new quality productivity forces can quickly integrate with local industries and promote the innovation and wide application of green technologies, thus significantly contributing to green development. In addition, the market mechanism in the eastern region is more perfect, and competition among enterprises is fierce, which prompts enterprises to constantly seek innovation, improve production efficiency, and reduce environmental pollution. At the same time, the government’s emphasis on and support for green development also provides a strong guarantee for the development of new quality productivity forces. Therefore, in the eastern region, the promotion of green development by new quality productivity forces is more significant. Looking at the central region, the economy has been developing rapidly in recent years, and the industrial structure has been continuously adjusted and optimized to improve the efficiency of resource use and reduce environmental pollution. At the same time, the central region has a large population and abundant labor resources, providing strong support for the development of new quality productivity forces. Compared with the eastern and central regions, the pace of economic development in the western region is slightly slower, the scientific and technological innovation capacity is slightly insufficient, there is a lack of advanced green technology and production methods, and the industrial structure is relatively homogeneous, mainly focusing on energy, raw materials, and other traditional areas. These industries have a high degree of dependence on resources and the environment, and green transformation is more difficult. As a result, green development in the western region faces many difficulties and lacks the intrinsic motivation to promote green transformation.

It is worth noting, however, that the western region is endowed with rich natural resources, and these advantages provide broad space and the potential for the development of new quality productivity forces in the region. Therefore, in future development, the western region should increase its efforts in scientific and technological innovation, strengthen its policy support and guidance, and promote the in-depth integration of new quality productivity forces with local industries, to facilitate green transformation and sustainable development.

6. Conclusions and Recommendations

This paper measures the level of new quality productivity forces in China based on the panel data of 30 provinces in China from 2012 to 2022 and explores the mechanism and spillover effect of new quality productivity forces on green development. The conclusions are as follows: (1) The improvement of new quality productivity forces can promote green development, and this conclusion still holds after the robustness test. (2) New quality productivity forces can improve the level of green development through the intermediary variables of technological progress (Inn) and industrial structure optimization (ISO). (3) New quality productivity forces can not only directly promote green development but also have a significant spatial spillover effect. (4) The analysis of heterogeneity reveals that the level of green development in the east and central regions have a greater positive impact on green development.

Based on the above theoretical analyses and empirical results, this paper makes the following recommendations.

First, strengthen scientific and technological innovation and promote green development through technological progress. The core of new quality productivity forces lies in scientific and technological innovation, and technological progress is the key to promoting green development. The government should formulate a series of policies on scientific and technological innovation, increase investment in research and the development of green technology, strengthen the construction of technological innovation platforms, and encourage enterprises, colleges, universities, and research institutes to carry out research and development and application promotion of green technology. In the process of technological innovation, special attention should be paid to the R&D and application of green technologies. The government should guide enterprises to adopt green technologies and promote the development of traditional industries in the direction of greening, low-carbonization, and recycling.

Secondly, the industrial structure should be optimized to achieve green transformation. The green transformation of traditional industries should be accelerated, the energy consumption and emissions of traditional industries should be reduced, and the effectiveness of resource use optimized through technological transformation, equipment renewal, and process innovation. It is necessary to actively promote the development of green industries and ecological circular economy, and tap new economic growth potential. The government should introduce relevant supportive policies to encourage enterprises to invest in green industries and circular economy fields and promote the rapid development of green industries. In the process of optimizing the industrial structure, the factor of regional heterogeneity should be fully considered. Different regions have differences in resource endowment, industrial structure, and environmental conditions, so differentiated industrial structure optimization policies should be formulated. For resource-rich regions, the development of a green energy industry should be encouraged; for ecologically fragile regions, the process of ecological conservation and restoration should be strengthened, and the development of green eco-tourism should be encouraged; for regions with a better industrial foundation, the development of existing industries in the direction of colonization should be promoted.

Thirdly, regional cooperation should be strengthened to promote the spatial spillover effect of green development. Governments should strengthen interregional green cooperation and exchanges and promote the spatial spillover effect of green development. It is necessary to establish a regional coordination mechanism for green development, enhance policy synergy and information sharing, and promote green synergistic development among regions. It is necessary to deepen interregional communication and win-win situation in green technology and promote the cross-regional dissemination and application of green technology. At the same time, it should also strengthen inter-regional ecological environment protection and restoration work, and jointly maintain the security and stability of the regional ecological environment. In the process of strengthening regional cooperation, cooperation, and exchanges should be strengthened for neighboring regions to jointly promote the spatial spillover effect of green development; for remote areas, cooperation and exchanges can be strengthened through the establishment of green industry cooperation parks and the development of green technology transfer.

7. Discussion

This study contributes valuable insights and perspectives in the field of exploring the impact of new quality productivity forces on green development at the provincial level in China. This study not only enriches the theoretical framework in this area but also provides important practical guidance. From the theoretical level, by incorporating new quality productivity forces and green development indicators into a comprehensive analytical framework, this paper reveals how new quality productivity forces can have a significant positive impact on green development by promoting technological progress, industrial structure optimization, and other paths. This finding attempts to provide some new insights into the study of the relationship between new quality productivity forces and green development and is expected to provide new perspectives for understanding the complex linkages between the two. At the practical level, this study provides useful references for policymakers and business decision-makers. By clarifying the key role of new quality productivity forces in promoting green development, this study encourages policymakers to increase their efforts to cultivate and support new quality productivity forces, while guiding enterprises to increase their investment in technological innovation, to achieve both economically and environmentally sustainable development. Compared with the previous literature, this study not only focuses on the direct impact of new quality productivity forces on green development but also explores in depth the underlying mechanisms, making the findings more in-depth and broader.

However, several limitations need to be addressed in order to provide reference and direction for subsequent research. Firstly, the study focuses on specific data at the provincial level in China, which inadvertently limits the generalizability of the findings. Given China’s unique development path, policy environment, and cultural background, its experience may not be directly applicable to other countries and regions, which to some extent undermines the broad applicability of the study’s findings. Second, new quality productivity forces, as a new and complex concept, are still in the process of being enriched and evolved. Similarly, as a multidimensional concept, the selection of quantitative indicators for green development also faces many challenges. Therefore, this study inevitably has deficiencies in the selection of quantitative indicators for new quality productivity forces and green development, which may have an impact on the accuracy and comprehensiveness of the research results. In addition, this study also realizes that there may be some endogeneity problems between the mediating and explanatory variables and new quality productivity forces, which limits the generalizability of the conclusions to a certain extent, and is a direction that future research needs to focus on. Finally, as far as the research methodology is concerned, there is still room for further optimization in this study. With the continuous progress of research technology and the deepening of cross-disciplinary integration, more advanced analytical tools and models can be introduced in the future to enrich the research means and broaden the research horizons, to reveal the intrinsic links and laws between new quality productivity forces and green development in a more in-depth manner.

In order to capture the dynamic relationship between new quality productivity forces and green development more comprehensively, future research can be deepened and expanded in the following directions. First, broader coverage at the geographical and industry levels. Future research should not be limited to the provincial level in China, but should gradually expand to the city, enterprise, and even more micro level, to reveal the specific impact of new quality productivity forces on green development in different geographical and industry contexts. Meanwhile, through cross-border comparative studies, the commonalities and differences between new quality productivity forces and green development in different political, economic, and cultural environments can be further explored. Second, optimize and improve the indicator system and strengthen the validity and reliability of the indicators to ensure that the conclusions of the study are accurate and credible. Third, innovate research methods and technical means. With the advancement of science and technology and the deepening of interdisciplinary intersections, future research should actively explore and apply new research methods and technical means to help reveal the complex relationship between new quality productivity forces and green development and to provide more precise and effective support for policy formulation and practical operation.