The Impact of Manufacturing Green Transformation on Public Health: Mechanisms and Empirical Evidence

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Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Manufacturing green transformation Public health Mediating effect Threshold effect 1.Introduction Against the backdrop of accelerating global climate change and the pursuit of sustainable development, green transformation of the manufacturing sector has emerged as a critical strategic imperative for nations seeking to balance environmental governance with economic growth.As the world's largest manufacturer, China faces significant environmental and health challenges following its rapid industrialization.The Report to the 20th National Congress of the Communist Party of China (CPC) explicitly prioritizes "promoting green and low-carbon economic and social development as a key lever for achieving high-quality development," while emphasizing the need to "prioritize public health within the national strategic framework" [ 1 ].Guided by the "dual carbon" goals, China's manufacturing industry is undergoing systemic transformation, driven by green technological innovation and centered on industrial structure optimization.This transformation is crucial not only for enhancing industrial competitiveness but also intrinsically linked to public health.Recent policy initiatives, including the *14th Five-Year Plan for Green Industrial Development* and Made in China 2025, have progressively strengthened the development of a green manufacturing system.These efforts, through stricter pollutant emission standards, promotion of cleaner production technologies, and adoption of circular economy models, have effectively mitigated the negative environmental externalities of industrial activities.Furthermore, the Outline of the "Healthy China 2030" Plan underscores the implementation strategy of "integrating health into all policies." Consequently, elucidating the mechanisms by which the green transformation of manufacturing affects public health holds significant strategic importance for achieving synergistic governance objectives encompassing "carbon reduction, pollution control, ecological expansion, economic growth, and health protection." Research on the impact of the green transformation of the manufacturing sector on residents' health has primarily evolved along two major directions. Direct link between manufacturing green transformation and public health.Manufacturing green transformation encompasses efforts to reduce pollution emissions through technological innovation, optimize industrial structure, and implement cleaner production processes, thereby minimizing environmental degradation.Studies indicate that this transformation directly enhances environmental quality by curbing pollutant emissions, consequently lowering public health risks associated with conditions such as respiratory diseases and cancer.Yang Li et al.employed a two-stage Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model, revealing that improvements in the green production efficiency of both industry and agriculture effectively reduce pollutant emissions[ 2 ] .Crucially, enhanced pollutant emission efficiency was found to be negatively correlated with tuberculosis incidence and the prevalence of foodborne diseases.i Yujun et al. identified an "inverted U-shaped" relationship between manufacturing agglomeration and health costs[ 3 ].Their findings suggest that initial stages of agglomeration increase pollution and harm health, while subsequent green transformation, facilitated by technological upgrading and environmental regulations, reduces emissions and leads to health improvements.Providing micro-level evidence, Yang Laikuo and Yan Ke confirmed that digital transformation significantly suppresses pollution emissions within the manufacturing sector[ 4 ].This reduction is achieved by boosting production efficiency and optimizing the energy structure, indirectly mitigating public health risks. Mediating pathways of manufacturing green transformation on public health.Manufacturing green transformation directly enhances environmental quality by reducing industrial pollutant emissions.Greenstone and Hanna demonstrated that industrial pollution control measures reduced PM2.5 concentrations by 10 µg/m³, leading to a 5%-7% reduction in infant mortality rates, thereby confirming the positive public health impact of such environmental improvements[ 5 ].Ye and Huang found that a reduction of 1 µg/m³ in air pollution was associated with a 19% decrease in the incidence of chronic diseases among the elderly.Similarly, Qiao and Ma reported that ecological civilization construction significantly improves public health, particularly for disadvantaged groups, primarily through the reduction of air pollutants.Heightened public attention to environmental issues exerts pressure on governments and enterprises to strengthen environmental governance[ 6 – 7 ] .Kou Dongxue et al. discovered that media attention acts as a moderator between digital transformation and green innovation efficiency, effectively strengthening the external oversight constraining corporate green behavior[ 8 ].Complementing this, Wang Haijie et al. found that the digital economy enhances public participation in environmental governance through improved information transparency, subsequently promoting green transformation within the manufacturing industry chain.Furthermore, manufacturing green transformation improves public health by driving innovation-led industrial structure upgrading[ 9 ].Shi Fengguang et al. indicated that green co-innovation fosters economic growth and health capital accumulation through the transformation of old and new growth drivers[ 10 ].Fu Chenyu and Yang Yanlin provided evidence, using the "Broadband China" policy as a quasi-natural experiment, that digital infrastructure promotes manufacturing green transformation by enhancing R&D investment and pollution control efficiency, ultimately leading to improvements in public health[ 11 ]. The review of existing literature reveals that while scholars acknowledge the positive impact of manufacturing green transformation on public health and have begun to explore both its direct effects and some indirect pathways, most studies remain focused on singular, direct environmental channels.There is insufficient exploration of the multifaceted transmission mechanisms through which green transformation indirectly influences public health within the complex socio-economic system.Therefore, building on prior research, this study utilizes provincial panel data from China (2012–2022), incorporating public environmental concern and industrial structure upgrading as mediating variables and introducing digitalization as a threshold variable.This approach allows for an empirical examination of the pathways by which manufacturing green transformation affects public health levels. 2.Theoretical Analysis and Research Hypotheses 2.1 Direct Impact of Manufacturing Green Transformation on Public Health The core objective of manufacturing green transformation is energy conservation and emission reduction, operationalized through reductions in energy consumption intensity and pollution emission intensity [ 12 ].This transformation drives technological innovation, which reduces both energy use per output unit and pollutant emissions, thereby improving environmental conditions for public health..Furthermore, manufacturing green transformation entails achieving cleaner production processes.This involves directly reducing emissions at the source and effectively mitigating harmful pollutant discharges through end-of-pipe treatment.Collectively, these actions contribute to sustained improvements in environmental quality, consequently lowering residents' disease risks associated with environmental pollution.Therefore, we propose: Hypothesis (H1) : Manufacturing green transformation significantly improves public health. 2.2 The Indirect Impact of Manufacturing Green Transformation on Public Health Public environmental concern, serving as a key form of informal environmental regulation, plays a crucial mediating role between manufacturing green transformation and public health.This occurs through several mechanisms: social pressure from public opinion, enhanced information transmission, and market incentives.1.Social Pressure from Public Opinion: The public expresses environmental demands through media, social platforms, and by exposing environmental incidents, generating significant social pressure.This pressure compels enterprises to improve their environmental performance.Empirical evidence indicates that a one-unit increase in public environmental concern leads to a 3.2% increase in corporate environmental investment and significantly enhances the intensity of green technological innovation [ 13 – 14 ].This pressure transmission mechanism is amplified in the digital era.For instance, Wu Libo et al. found that a 10% increase in the smog-related search index correlates with a 4.5% rise in local government investment in environmental protection infrastructure[ 15 ].2.Enhanced Information Transmission: Public environmental concern promotes the institutionalization of environmental information disclosure.To mitigate reputational risks, firms become more inclined to voluntarily disclose pollutant emission data and mitigation strategies [ 16 ].The resulting improvement in information transparency reduces information asymmetry between residents and pollution sources.This empowers individuals to adopt protective behaviors (e.g., relocation, use of air purifiers), directly lowering their health risks.3.Market Incentives: Public environmental preferences influence corporate behavior via market mechanisms.Growing consumer demand for environmentally friendly products—such as low-carbon certified goods and organic items—prompts enterprises to increase investment in green innovation and optimize production processes.This shift drives industry-wide green transformation, indirectly improving the environmental conditions affecting public health. Industrial structure upgrading, as a core pathway for the green transformation of the manufacturing industry, significantly reduces pollution emission intensity through optimized resource allocation, the phase-out of high-pollution industries, and the development of clean technology-intensive sectors. Thereby, it contributes to improved environmental quality and enhanced public health. More specifically, such upgrading facilitates industrial rationalization by shifting production factors from traditional manufacturing sectors—characterized by high pollution and energy consumption—to modern service industries that are more efficient and less polluting. Empirical evidence indicates that a 1% increase in the share of the tertiary industry is associated with a 0.8% reduction in regional PM2.5 concentration [ 17 ]. Concurrently, industrial upgrading fosters the growth of high-tech industries and green supply chains, replacing conventional production processes with cleaner technologies. For example, in the Yangtze River Economic Belt, a one-unit increase in the industrial structure advancement index corresponds to a 12% decrease in industrial wastewater discharge intensity [ 18 ].Therefore, we propose: Hypothesis (H2) : Public environmental concern and industrial structure upgrading serve as mediating mechanisms through which manufacturing green transformation improves public health outcomes. 2.3 The Threshold Effect of Digitalization on the Health Impact of Manufacturing Green Transformation The health impact of manufacturing green transformation (MGT) on residents is contingent upon the level of digitalization.Under low digitalization levels, inadequate infrastructure, inefficient technology application, and constrained environmental monitoring capabilities hinder the effective deployment of green technologies.For instance, in regions with underdeveloped digital foundations (e.g., central and western China), enterprises face difficulties in implementing real-time pollution monitoring via IoT systems[ 19 ], resulting in superficial implementation of transformation measures.Conversely, when digitalization exceeds a critical threshold, technologies such as intelligent sensors, big data platforms, and cloud computing enable comprehensive dynamic monitoring of pollution sources[ 4 ].This facilitates precise identification of high-pollution processes and optimization of emission reduction strategies, significantly amplifying the marginal health benefits of MGT.Moreover, digitalization enhances "government-enterprise-public" collaborative governance efficacy:Low-digitalization phase: Reliance on manual government inspections, delayed corporate environmental disclosures, and restricted public participation channels collectively impair policy implementation efficiency[ 20 ].Post-threshold phase: Blockchain ensures carbon data transparency and traceability[ 21 ], digital government platforms enable real-time pollution data disclosure, and social media empowers public supervision, collectively compelling substantive green transformation[ 22 ].Therefore, we propose: Hypothesis (H3) : The impact of manufacturing green transformation on public health exhibits a digitalization-driven threshold effect, becoming significantly positive only when digitalization surpasses a critical level. 3.Study Design 3.1 Model Specification 3.1.1 Baseline Regression Model To examine the direct impact of manufacturing green transformation (MGT) on public health, we establish the following fixed-effects model: $$\:{\text{Public}\text{}\text{Health}}_{\text{it}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}{\text{Green}}_{\text{it}}\text{+}{\text{β}}_{\text{2}}{\text{Controls}}_{\text{it}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{γ}}_{\text{t}}\text{+}{\text{ε}}_{\text{it}}$$ 1 \(\:{\text{Public}\text{}\text{Health}}_{\text{it}\text{}}\) denotes the health level of residents in province i year t ,measured by population mortality rate; \(\:{\text{Green}}_{\text{it}}\) represents the level of manufacturing green transformation༛ \(\:{\text{Controls}}_{\text{it}}\) is a vector of control variables༛ \(\:{\text{μ}}_{\text{i}}\) and \(\:{\text{γ}}_{\text{t}}\) denote province and year fixed effects, respectively༛ \(\:{\text{ε}}_{\text{it}}\) is the idiosyncratic error term。 3.1.2Mediation Effect Model To identify transmission mechanisms: $$\:{\text{Mediator}}_{\text{it}}\text{=}{\text{α}}_{\text{0}}\text{+}{\text{α}}_{\text{1}}{\text{Green}}_{\text{it}}\text{+}{\text{α}}_{\text{2}}{\text{Controls}}_{\text{it}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{γ}}_{\text{t}}\text{+}{\text{ε}}_{\text{it}}$$ 2 $$\:{\text{Public}\text{}\text{Health}}_{\text{it}}\text{=}{\text{δ}}_{\text{0}}\text{+}{\text{δ}}_{\text{1}}{\text{Green}}_{\text{it}}\text{+}{\text{δ}}_{\text{2}}{\text{Mediator}}_{\text{it}}\text{+}{\text{δ}}_{\text{3}}{\text{Controls}}_{\text{it}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{γ}}_{\text{t}}\text{+}{\text{ε}}_{\text{it}}\text{}$$ 3 The mediator variable \(\:{\text{Mediator}}_{\text{it}}\) captures either public environmental concern or industrial structure upgrading. 3.1.3 Threshold Effect Model To test nonlinear effects: $$\:{\text{Public}\text{}\text{Health}}_{\text{it}}\text{=}{\lambda}_{\text{1}}{\text{Green}}_{\text{it}}\text{×}\text{I}\text{（}{\text{Digi}}_{\text{it}}\text{≤}\theta\text{）+}{\lambda}_{\text{2}}{\text{Green}}_{\text{it}}\text{×}\text{I}\text{（}{\text{Digi}}_{\text{it}}\text{>}\theta\text{）+}\text{β}{\text{controls}}_{\text{it}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{γ}}_{\text{t}}\text{+}{\text{ε}}_{\text{it}}$$ 4 \(\:{\text{Digi}}_{\text{it}}\) is the digitalization level of manufacturing, \(\:\theta\) is the estimated threshold value, \(\:\text{I}\text{（·）}\) denotes the indicator function (1 if condition true, 0 otherwise). 3.2 Variable Selection 3.2.1 Dependent Variable Residents' health level is measured by crude death rate (‰, per thousand population), a widely adopted indicator in public health research. 3.2.2 Core Independent Variable Manufacturing green transformation ( \(\:{\text{Green}}_{\text{it}}\) ) is quantified through a comprehensive evaluation index system constructed along five dimensions:1.Green InnovationFinancial 2.Support Resource Consumption 3.Pollution Emissions 4.Ecological Protection.The specific measurement framework with indicator weights and calculation methodology is detailed in Table 1 . Table 1 Evaluation Index System for Manufacturing Green Transformation Manufacturing Green Transformation Level Tier 1 Indicator Tier 2 Indicator Green Innovation Green invention patent applications + Green utility model patent applications + Financial Support Green credit ratio (Provincial green credit volume / Total provincial credit volume) + Green bond penetration (Total green bond issuance / Total bond issuance) + Green fund share (Market capitalization of green funds / Total fund market capitalization) + Resource Consumption Electricity intensity (Power consumption / Industrial value-added) - Water intensity (Industrial water use / Regional GDP) - Energy intensity (Energy consumption / Regional GDP) - Pollution Emissions COD emission intensity (Chemical oxygen demand in wastewater / Industrial value-added) - SO₂ emission intensity (Sulfur dioxide emissions / Industrial value-added) - Industrial solid waste intensity (General industrial solid waste / Industrial value-added) - Ecological Protection Industrial pollution control investment ratio to GDP (Industrial pollution control investment / GDP) + Comprehensive utilization rate of industrial solid waste + Environmental fiscal expenditure ratio (Local environmental protection expenditure / General fiscal budget expenditure) + 3.2.3 Mediating Variables Public environmental concern:Measured by normalizing Baidu Search Index results for the keyword "environmental pollution". Industrial structure upgrading:Quantified as the ratio of tertiary industry value-added to secondary industry value-added. 3.2.4 Control Variables To mitigate confounding effects, we control for eight provincial-level factors:Economic development level 、Urbanization rate 、Health expenditure、Medical resources 、Elderly dependency ratio、Pension insurance coverage rate、Education attainment、Climate conditions 3.2.5 Threshold Variable Manufacturing digitalization level - constructed through a multidimensional index system comprising:Digital infrastructure、Digital application、Digital innovation、Digital effectiveness、Complete variable definitions are provided in Table 2 . Table 2 Evaluation Index System for Manufacturing Digitalization Level. Manufacturing Digitalization Level Tier 1 Indicator Tier 2 Indicator Digital Infrastructure Number of registered domains + IPv4 address count + Broadband access ports + Mobile phone penetration rate + Long-distance fiber optic cable density + Mobile base station density + Digital equipment investment + Digital Application Digitally enabled enterprises + Websites per 100 enterprises + E-commerce adoption rate + Digital inclusive finance index + ICT sector employment + Telecom revenue as % of GDP + E-commerce turnover as % of GDP + Computers per 100 employees + Digital Innovation Full-time equivalent R&D personnel in industrial enterprises + R&D personnel in high-tech industries + R&D projects in industrial enterprises + Intramural R&D expenditure in high-tech industries + R&D expenditure in industrial enterprises + Technology transaction value + Granted patents + Digital Effectiveness Software revenue as % of GDP + IT services revenue as % of GDP + High-tech industry revenue share + Revenue per employee in high-tech industries + Total profits in high-tech industries + Table 3 Variable Definitions and Descriptive Statistics. VariableProperty VariableName VariableDefinition Mean Std.Dev. Min Max Dependent variable Public health level Crude death rate (‰) 6.395 0.980 4.26 9.09 Independent variable Manufacturing green transformation Composite index by TOPSIS-entropy weight method (0–1) 0.194 0.117 0.058 0.670 Control variables Economic development GDP per capita (CNY) 10.908 0.445 9.849 12.155 Urbanization rate Urban population / Total population (%) 0.607 0.117 0.363 0.896 Health expenditure Healthcare expenditure / GDP (%) 0.021 0.009 0.008 0.057 Medical resources Hospital beds per 10,000 people 56.908 11.230 33.54 84.31 Elderly dependency ratio Population aged ≥ 65 / Working-age population (%) 15.914 4.373 8.800 28.800 Pension insurance coverage Pension insurance participants / Total population (%) 0.349 0.136 0.030 0.575 Education attainment Average years of schooling (years) 9.329 0.909 7.474 12.782 Climate conditions Annual mean temperature (°C) 12.793 6.134 -2.851 25.235 Mediating variables Public environmental concern Baidu Search Index for "environmental pollution" (standardized) 108.93 37.852 17.773 215.38 Industrial structure upgrading Tertiary sector value-added / Secondary sector value-added 1.388 0.750 0.611 5.244 Threshold variable Manufacturing digitalization Composite index from Table 2 evaluation system (0–1) 0.117 0.109 0.013 0.681 3.3 Data Description This study employs panel data covering 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan due to data limitations) from 2012 to 2022, selected based on data availability and completeness.Primary data sources include the China Statistical Yearbook, China Industrial Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Insurance Statistical Yearbook, and the China Research Data Service Platform (CNRDS).Isolated missing values were addressed through linear interpolation. 4 Empirical Results and Analysis 4.1 Baseline Regression Analysis Table 4 Baseline Regression Results. Variable (1) (2) Coeff. Std.Err. Coeff. Std.Err. Manufacturing green transformation -0.549** 0.273 -0.734*** 0.283 Economic development -0.656 0.616 Urbanization rate -0.095 2.054 Health expenditure -5.277 12.428 Medical resources 0.031*** 0.010 Elderly dependency ratio 0.089*** 0.021 Pension insurance coverage 0.683 1.166 Education attainment -0.181 0.159 Climate conditions -0.001 0.004 Constant 4.916*** 0.174 12.494* 7.369 Province fixed effects Yes Yes Year fixed effects Yes Yes R-squared 0.854 0.885 Observations 330 330 Notes: ① ** and * denote statistical significance at the 5% and 10% levels, respectively.② Robust standard errors are reported in parentheses. This study employs a two-way fixed effects model to examine the relationship between manufacturing green transformation (MGT) and public health.As shown in Table 4 , MGT exerts a significantly positive impact on public health (5% level) in the baseline specification with only fixed effects.When control variables are introduced, this positive effect strengthens substantially, achieving statistical significance at the 1% level.This enhanced significance after accounting for socioeconomic confounders suggests that the net health benefits of MGT become more pronounced, potentially because certain controls (e.g., medical resources) inherently correlate with green transformation—controlling for these variables isolates MGT's independent contribution.Notably, while medical resources demonstrate the expected positive coefficient, their effect size is considerably smaller than MGT's direct impact, implying that environmental interventions may outperform traditional healthcare inputs.The significantly positive coefficient of the elderly dependency ratio further aligns with global aging challenges, underscoring the necessity for integrated policy coordination between green transformation and pension security systems. 4.2 Robustness Test To ensure the reliability of baseline regression results and precisely characterize the impact of manufacturing green transformation (MGT) on public health, robustness tests were conducted through lagged variables, truncation procedures, and exclusion of special years.First, a one-period lagged MGT index was adopted to mitigate potential reverse causality.Second, all variables underwent 1% winsorization to eliminate outlier distortion.Third, samples from 2012–2019 were retained to exclude COVID-19 pandemic interference during 2020—a significant public health anomaly.Results presented in Columns (1)-(3) of Table 5 consistently confirm the resilience of our core findings. Table 5 Robustness Test Results. Variable LaggedVariables Winsorization Exclude2020 (1) (2) (5) Manufacturing green transformation -0.660** 0.180** (0.291) (2.06) Lagged MGT -0.920*** (0.319) Constant 13.288 7.820 -5.090** (8.690) (7.348) (-2.22) Control variables Yes Yes Yes Province FE Yes Yes Yes Year FE Yes Yes Yes R-squared 0.889 0.884 0.172 Observations 330 330 689 Notes: Robust standard errors in parentheses; same for subsequent tables. The robustness tests reveal three key insights: First, when employing a one-period lagged manufacturing green transformation (MGT) variable, the coefficient magnitude increases further, suggesting persistent and potentially cumulative health benefits over time.Second, after 1% winsorization, the coefficient remains directionally consistent with baseline results but moderately attenuates, indicating limited outlier influence while highlighting the need to scrutinize region-specific dynamics in high-pollution clusters or green transformation pioneers.Third, exclusion of the COVID-19 pandemic period (2020) renders the MGT coefficient statistically insignificant though directionally positive, likely due to pandemic-induced distortions in health data—including healthcare system disruptions and mortality reporting anomalies—which may obscure long-term environmental health effects. 4.3 Mediation Effect Analysis Table 6 Mediation Effect Results. Variable PublicEnv.ConcernPathway IndustrialUpgradingPathway (1)Publicenvironmentalconcern (2)DeathRate (3)Industrialstructureupgrading (4)DeathRate Manufacturing green transformation 0.194** -0.637** 0.296*** -0.488* (0.091) (0.284) (0.096) (0.277) Public environmental concern -0.500* (0.268) Industrial structure upgrading -0.829*** (0.214) Constant -0.970 12.009 11.436*** 21.978*** (1.158) (7.305) (2.080) (7.201) Control variables Yes Yes Yes Yes Province FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes R-squared 0.958 0.276 0.980 0.893 Observations 330 330 330 330 This study examines the dual mediating pathways proposed in Hypothesis H2—where public environmental concern and industrial structure upgrading transmit the health effects of manufacturing green transformation (MGT)—using a formal mediation model.Table 6 results demonstrate that MGT significantly enhances public environmental concern (coeff.= 0.194, p < 0.05; Column 1), which subsequently reduces mortality rates (coeff.= -0.500, p < 0.10; Column 2), confirming its mediating role through heightened environmental awareness that motivates individual pollution-avoidance behaviors and community-led emission monitoring.Simultaneously, MGT drives substantial industrial restructuring (coeff.= 0.296, p < 0.01; Column 3), with upgrading significantly lowering mortality (coeff.= -0.829, p < 0.01; Column 4) by transitioning high-pollution manufacturing to cleaner service sectors—reducing industrial emissions while improving health behaviors through service-sector employment growth.These robust dual-pathway mechanisms collectively validate H2. 4.4 Threshold Effect Analysis This study employs a bootstrap test with 500 replications to examine the threshold effect of manufacturing digitalization on the health impact of green transformation.As summarized in Table 7 , a single-threshold model demonstrates statistical significance (F = 23.59, p = 0.080) at the 10% level, while the double-threshold model fails to reject the null hypothesis (p = 0.916).These results confirm the existence of a statistically significant single threshold effect in the relationship between manufacturing digitalization and the health returns from green transformation. Table 7 Threshold Effect Test. Model F-stat p-value BootstrapReps 1%CriticalValue 5%CriticalValue 10%CriticalValue Single threshold 23.59* 0.080 500 20.978 26.812 39.775 Double threshold 3.34 0.916 500 22.625 32.794 74.792 Table 8 Single Threshold Model Results. Variable Fertilizerapplication Coeff. Std.Err. Manufacturing digitalization ≤ 0.343 -0.242 0.412 Manufacturing digitalization > 0.343 -1.687** 0.646 Constant 18.173 12.647 Control variables Included Province FE Included Year FE Included Observations 330 R2 0.660 Building upon the threshold analysis, we validated the model by incorporating the manufacturing digitalization threshold (0.343) and corresponding coefficients (Table 8 ).Results reveal a structural shift in green transformation's health impact when digitalization exceeds this critical value: Below the threshold (≤ 0.343), green transformation shows an insignificant negative coefficient (β=-0.242, p > 0.10), indicating limited health benefits due to technological immaturity, monitoring deficiencies, and implementation costs that constrain pollution reduction efficacy.Conversely, above-threshold digitalization (> 0.343) strengthens the effect substantially (β=-1.687, p < 0.01), where intelligent monitoring and clean production optimization enhance environmental governance efficiency, significantly curbing hazardous emissions and amplifying health gains through reduced pollution exposure. Consequently, the threshold analysis confirms a statistically significant digitalization threshold governing manufacturing green transformation's impact on public health.Below this critical value, health improvements remain statistically insignificant; only when digitalization surpasses the threshold does green transformation yield significant health improvements.These findings conclusively validate Hypothesis H3. 5 Conclusions and Implications Using provincial panel data from China (2012–2022), this study systematically examines manufacturing green transformation's impact on public health and its transmission mechanisms.Key conclusions emerge: (1)Manufacturing green transformation significantly improves public health outcomes. (2)This health impact operates through dual mediating channels: public environmental concern and industrial structure upgrading. (3)A significant digitalization threshold governs the health effects, with impacts becoming statistically meaningful only above critical digital capability levels. Corresponding policy implications follow: 5.1 Deepening Green-Digital Integration in Manufacturing to Unleash Synergistic Governance Effects This integration serves as a critical pathway for enhancing environmental governance efficacy.Policymakers should strengthen top-level design by incorporating both priorities into a unified policy framework, implementing a "digitalization-empowered green transformation" development strategy.Institutional mechanisms for cross-departmental coordination must be established, integrating resources from industrial, environmental, and technological sectors to form policy coherence.Accelerate deployment of frontier technologies—including IoT, big data, and AI—for real-time pollution monitoring and emission reduction.Infrastructure development in central and western regions requires prioritization through 5G network expansion and industrial internet platforms to bridge regional technological disparities.Furthermore, establish eastern-developed-region knowledge transfer programs featuring technical assistance and shared digital governance frameworks, enabling less-developed regions to surpass digitalization thresholds and fully realize green transformation's health co-benefits. 5.2 Accelerating Industrial Restructuring to Build a Green Low-Carbon Industrial Ecosystem Industrial restructuring constitutes the fundamental pathway for achieving coordinated development between manufacturing green transformation and public health.Market-based mechanisms and administrative measures should be synergistically deployed to facilitate orderly phase-outs of energy-intensive and high-pollution industries, thereby creating development space for green emerging sectors.Concurrently, traditional manufacturing must be steered toward high-end, intelligent, and eco-friendly production through enterprise adoption of digital-green technologies, enhancing both operational efficiency and environmental performance.Strengthening industrial chain collaboration is critical—building vertically integrated green supply chains will reduce aggregate pollution intensity via technological spillovers and economies of scale, enabling simultaneous optimization of industrial and energy structures.Complementary to these efforts, expanding green financial instruments (including credit facilities and bond markets) must strategically direct social capital toward renewable energy, environmental technologies, and advanced equipment manufacturing sectors. 5.3 Strengthening Public Participation Mechanisms to Activate Societal Co-Governance in Environmental Management Public environmental awareness serves as a critical catalyst for activating the intrinsic drivers of environmental governance.Multi-tiered public participation platforms should be established alongside enhanced environmental information disclosure systems, disseminating real-time air/water quality data and health risk alerts through official applications and social media to elevate ecological literacy.Implementing incentivized reporting mechanisms for environmental violations will empower citizen oversight of corporate emissions, creating a responsive "public supervision-rapid action" governance loop.Concurrently, green consumption must be promoted through carbon credit systems and eco-certification incentives, transforming consumer preferences into market pressure for corporate environmental upgrades.Complementary environmental education initiatives—integrating community workshops and K-12 curricula—will cultivate lasting ecological consciousness, ultimately forging a sustainable governance ecosystem characterized by governmental leadership, corporate accountability, and civic engagement. 5.4 Implementing Regionally Differentiated Policies to Overcome Digitalization Threshold Constraints in Central-Western China Addressing the digital infrastructure deficits and delayed green transition in central-western China requires implementing a regionally differentiated strategy of "precision policymaking and phased advancement." Targeted fiscal interventions should prioritize industrial internet deployment and smart environmental monitoring platforms to reduce corporate digitalization costs.The innovative "flying land economy" model warrants expansion—integrating eastern technological capabilities with western renewable resources, exemplified by deploying intelligent operations systems in western photovoltaic bases to optimize clean energy utilization.Concurrently, enhanced interregional ecological compensation mechanisms must be established, utilizing carbon trading markets and horizontal fiscal transfers to balance developmental equities and ensure equitable access to sustainability dividends. Declarations Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by National Social Science Fund Project,grant number 19BTJ039. Author Contribution X. Li: Conceptualization, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing.Y. Deng: Methodology, Software, Investigation, Data curation, Formal analysis, Visualization.All authors reviewed and approved the final manuscript. Data Availability he datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Xi, J. Hold High the Great Banner of Socialism with Chinese Characteristics and Strive in Unity for the Comprehensive Construction of a Modern Socialist Country: Report at the 20th National Congress of the Communist Party of China (.M.People's Publishing House, 2022). Yang, L., Huang, S. & Zhang T.Research on green production efficiency and health efficiency in industry and agriculture: Based on a parallel two-stage SBM-DEA model. J. Econ. Manage. 37 (5), 45–54 (2023). Ji, Y. & Wei, C. Health costs of manufacturing agglomeration in China: Internal mechanism and empirical test. J. J. 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J.Journal of Beijing University of Technology (Social Sciences Edition), 24 (2): 48–66. (2024). Wang, H., Zhu, X. & Wang Q.Impact of digital economy on green transformation of manufacturing industrial chains: Empirical evidence from urban-level data. J. J. Manage. 38 (2), 88–100 (2025). Shi, F., Zhou, M. & Xu B.Green collaborative innovation, transformation of old and new growth drivers, and regional economic growth. J. Stat. Res. 40 (7), 33–44 (2023). Fu, C. & Yang Y.Digital empowerment and green transformation of manufacturing enterprises: Analysis based on the Broadband China demonstration city policy. J. Res. Econ. Manage. 46 (3), 94–111 (2025). Dai, X. & Yang, S. D. empowerment, sources of digital input, and green transformation of manufacturing. J. China Industrial Econ. , (9): 83–101. (2022). Yang, L., Gan, Q. & Ma D.Public environmental concern and corporate environmental investment: Moderating role of green image.J.Finance and Accounting Monthly, (8): 33–40. (2020). Xu, J., Ye, F. & Shang L.Impact of public environmental concern on corporate carbon performance. J. Chin. J. Manage. 21 (6), 865–875 (2024). Wu, L., Yang, M. & Sun K.Impact of public environmental concern on corporate and governmental environmental governance. J.China Population. Resour. Environ. 32 (2), 1–14 (2022). Zhao, Y. & Xu, Y. P. concern, industry competition, and environmental information disclosure: Empirical evidence from Shanghai manufacturing industry. J. Communication Finance Acc. , (9): 78–80. (2016). Yan, T. & Zhu M.Impact of technological innovation and industrial structure upgrading on environmental pollution. J. J. Chongqing Univ. (Social Sci. Edition) . 29 (5), 70–84 (2023). Li, Q. Does industrial upgrading promote ecological environment optimization? Evidence from panel data of 108 cities in the Yangtze River Economic Belt.J.Finance and Trade Research, 29 (12): 39–47. (2018). Cao, Y., Hu, H. W. G. & Wang, S. How does digitalization drive green transformation in manufacturing enterprises? An exploratory case study from the perspective of resource orchestration theory. J. J. Manage. World . 39 (3), 96–126 (2023). Li, T., Ma, Y., Zheng, X., Song, Z. & Wang Y.Public environmental concern, environmental performance, and environmental information disclosure: Evidence from China’s high-pollution industries. J. J. Technol. Econ. Manage. Res. , (5): 85–89. (2023). Ma, L., Liu, S. & Zheng M.Corporate digital transformation, green innovation, and carbon performance: Moderating roles of carbon emission trading policy and public. Environ. concern. J. R&D Manage. 36 (2), 63–73 (2024). Lin, C. & Wu Q.Does digital transformation promote corporate green transformation? J. West. Forum . 34 (4), 94–110 (2024). Additional Declarations No competing interests reported. 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Xin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfmbm459//rORk2dvIFKLZDtbGjMDW5qxYc8BIrUYnOcxA2o5nMhwI4FYLYcZzB4X8KQlMM58vPEGQ41NNGGHHWZIN54hYZPHLp1WbMFwLC23gZAWvsMMByR4DNKKGWfnmEkwNhwmrIXhMGODBE/C4cSGm2eI1CJwmJlNmucAUMsNHiK1SDazMRvObAAFMtAvCcT4hZ///McHHxtAUXl4440PNTZE+AUJGEgkkKIcooVUHaNgFIyCUTAyAAD3sj8J6aprRwAAAABJRU5ErkJggg==","orcid":"","institution":"Party School of the CPC Taizhou Municipal Committee","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Xin","suffix":""},{"id":524626977,"identity":"450f8bce-8f4f-43e8-90ea-0662c2cfe97a","order_by":1,"name":"Deng Yahui","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Deng","middleName":"","lastName":"Yahui","suffix":""}],"badges":[],"createdAt":"2025-09-15 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16:12:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1393910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7615650/v1/6756a531-8f42-4d4c-80ad-9f135ea39f9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Manufacturing Green Transformation on Public Health: Mechanisms and Empirical Evidence","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eAgainst the backdrop of accelerating global climate change and the pursuit of sustainable development, green transformation of the manufacturing sector has emerged as a critical strategic imperative for nations seeking to balance environmental governance with economic growth.As the world's largest manufacturer, China faces significant environmental and health challenges following its rapid industrialization.The Report to the 20th National Congress of the Communist Party of China (CPC) explicitly prioritizes \"promoting green and low-carbon economic and social development as a key lever for achieving high-quality development,\" while emphasizing the need to \"prioritize public health within the national strategic framework\" [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Guided by the \"dual carbon\" goals, China's manufacturing industry is undergoing systemic transformation, driven by green technological innovation and centered on industrial structure optimization.This transformation is crucial not only for enhancing industrial competitiveness but also intrinsically linked to public health.Recent policy initiatives, including the *14th Five-Year Plan for Green Industrial Development* and Made in China 2025, have progressively strengthened the development of a green manufacturing system.These efforts, through stricter pollutant emission standards, promotion of cleaner production technologies, and adoption of circular economy models, have effectively mitigated the negative environmental externalities of industrial activities.Furthermore, the Outline of the \"Healthy China 2030\" Plan underscores the implementation strategy of \"integrating health into all policies.\" Consequently, elucidating the mechanisms by which the green transformation of manufacturing affects public health holds significant strategic importance for achieving synergistic governance objectives encompassing \"carbon reduction, pollution control, ecological expansion, economic growth, and health protection.\"\u003c/p\u003e\u003cp\u003eResearch on the impact of the green transformation of the manufacturing sector on residents' health has primarily evolved along two major directions.\u003c/p\u003e\u003cp\u003eDirect link between manufacturing green transformation and public health.Manufacturing green transformation encompasses efforts to reduce pollution emissions through technological innovation, optimize industrial structure, and implement cleaner production processes, thereby minimizing environmental degradation.Studies indicate that this transformation directly enhances environmental quality by curbing pollutant emissions, consequently lowering public health risks associated with conditions such as respiratory diseases and cancer.Yang Li et al.employed a two-stage Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model, revealing that improvements in the green production efficiency of both industry and agriculture effectively reduce pollutant emissions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] .Crucially, enhanced pollutant emission efficiency was found to be negatively correlated with tuberculosis incidence and the prevalence of foodborne diseases.i Yujun et al. identified an \"inverted U-shaped\" relationship between manufacturing agglomeration and health costs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Their findings suggest that initial stages of agglomeration increase pollution and harm health, while subsequent green transformation, facilitated by technological upgrading and environmental regulations, reduces emissions and leads to health improvements.Providing micro-level evidence, Yang Laikuo and Yan Ke confirmed that digital transformation significantly suppresses pollution emissions within the manufacturing sector[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].This reduction is achieved by boosting production efficiency and optimizing the energy structure, indirectly mitigating public health risks.\u003c/p\u003e\u003cp\u003eMediating pathways of manufacturing green transformation on public health.Manufacturing green transformation directly enhances environmental quality by reducing industrial pollutant emissions.Greenstone and Hanna demonstrated that industrial pollution control measures reduced PM2.5 concentrations by 10 \u0026micro;g/m\u0026sup3;, leading to a 5%-7% reduction in infant mortality rates, thereby confirming the positive public health impact of such environmental improvements[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Ye and Huang found that a reduction of 1 \u0026micro;g/m\u0026sup3; in air pollution was associated with a 19% decrease in the incidence of chronic diseases among the elderly.Similarly, Qiao and Ma reported that ecological civilization construction significantly improves public health, particularly for disadvantaged groups, primarily through the reduction of air pollutants.Heightened public attention to environmental issues exerts pressure on governments and enterprises to strengthen environmental governance[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] .Kou Dongxue et al. discovered that media attention acts as a moderator between digital transformation and green innovation efficiency, effectively strengthening the external oversight constraining corporate green behavior[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Complementing this, Wang Haijie et al. found that the digital economy enhances public participation in environmental governance through improved information transparency, subsequently promoting green transformation within the manufacturing industry chain.Furthermore, manufacturing green transformation improves public health by driving innovation-led industrial structure upgrading[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].Shi Fengguang et al. indicated that green co-innovation fosters economic growth and health capital accumulation through the transformation of old and new growth drivers[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Fu Chenyu and Yang Yanlin provided evidence, using the \"Broadband China\" policy as a quasi-natural experiment, that digital infrastructure promotes manufacturing green transformation by enhancing R\u0026amp;D investment and pollution control efficiency, ultimately leading to improvements in public health[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe review of existing literature reveals that while scholars acknowledge the positive impact of manufacturing green transformation on public health and have begun to explore both its direct effects and some indirect pathways, most studies remain focused on singular, direct environmental channels.There is insufficient exploration of the multifaceted transmission mechanisms through which green transformation indirectly influences public health within the complex socio-economic system.Therefore, building on prior research, this study utilizes provincial panel data from China (2012\u0026ndash;2022), incorporating public environmental concern and industrial structure upgrading as mediating variables and introducing digitalization as a threshold variable.This approach allows for an empirical examination of the pathways by which manufacturing green transformation affects public health levels.\u003c/p\u003e"},{"header":"2.Theoretical Analysis and Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Direct Impact of Manufacturing Green Transformation on Public Health\u003c/h2\u003e\u003cp\u003eThe core objective of manufacturing green transformation is energy conservation and emission reduction, operationalized through reductions in energy consumption intensity and pollution emission intensity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].This transformation drives technological innovation, which reduces both energy use per output unit and pollutant emissions, thereby improving environmental conditions for public health..Furthermore, manufacturing green transformation entails achieving cleaner production processes.This involves directly reducing emissions at the source and effectively mitigating harmful pollutant discharges through end-of-pipe treatment.Collectively, these actions contribute to sustained improvements in environmental quality, consequently lowering residents' disease risks associated with environmental pollution.Therefore, we propose:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003e(H1)\u003c/b\u003e: Manufacturing green transformation significantly improves public health.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 The Indirect Impact of Manufacturing Green Transformation on Public Health\u003c/h2\u003e\u003cp\u003ePublic environmental concern, serving as a key form of informal environmental regulation, plays a crucial mediating role between manufacturing green transformation and public health.This occurs through several mechanisms: social pressure from public opinion, enhanced information transmission, and market incentives.1.Social Pressure from Public Opinion: The public expresses environmental demands through media, social platforms, and by exposing environmental incidents, generating significant social pressure.This pressure compels enterprises to improve their environmental performance.Empirical evidence indicates that a one-unit increase in public environmental concern leads to a 3.2% increase in corporate environmental investment and significantly enhances the intensity of green technological innovation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].This pressure transmission mechanism is amplified in the digital era.For instance, Wu Libo et al. found that a 10% increase in the smog-related search index correlates with a 4.5% rise in local government investment in environmental protection infrastructure[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].2.Enhanced Information Transmission: Public environmental concern promotes the institutionalization of environmental information disclosure.To mitigate reputational risks, firms become more inclined to voluntarily disclose pollutant emission data and mitigation strategies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].The resulting improvement in information transparency reduces information asymmetry between residents and pollution sources.This empowers individuals to adopt protective behaviors (e.g., relocation, use of air purifiers), directly lowering their health risks.3.Market Incentives: Public environmental preferences influence corporate behavior via market mechanisms.Growing consumer demand for environmentally friendly products\u0026mdash;such as low-carbon certified goods and organic items\u0026mdash;prompts enterprises to increase investment in green innovation and optimize production processes.This shift drives industry-wide green transformation, indirectly improving the environmental conditions affecting public health.\u003c/p\u003e\u003cp\u003eIndustrial structure upgrading, as a core pathway for the green transformation of the manufacturing industry, significantly reduces pollution emission intensity through optimized resource allocation, the phase-out of high-pollution industries, and the development of clean technology-intensive sectors. Thereby, it contributes to improved environmental quality and enhanced public health. More specifically, such upgrading facilitates industrial rationalization by shifting production factors from traditional manufacturing sectors\u0026mdash;characterized by high pollution and energy consumption\u0026mdash;to modern service industries that are more efficient and less polluting. Empirical evidence indicates that a 1% increase in the share of the tertiary industry is associated with a 0.8% reduction in regional PM2.5 concentration [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Concurrently, industrial upgrading fosters the growth of high-tech industries and green supply chains, replacing conventional production processes with cleaner technologies. For example, in the Yangtze River Economic Belt, a one-unit increase in the industrial structure advancement index corresponds to a 12% decrease in industrial wastewater discharge intensity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Therefore, we propose:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003e(H2)\u003c/b\u003e: Public environmental concern and industrial structure upgrading serve as mediating mechanisms through which manufacturing green transformation improves public health outcomes.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The Threshold Effect of Digitalization on the Health Impact of Manufacturing Green Transformation\u003c/h2\u003e\u003cp\u003eThe health impact of manufacturing green transformation (MGT) on residents is contingent upon the level of digitalization.Under low digitalization levels, inadequate infrastructure, inefficient technology application, and constrained environmental monitoring capabilities hinder the effective deployment of green technologies.For instance, in regions with underdeveloped digital foundations (e.g., central and western China), enterprises face difficulties in implementing real-time pollution monitoring via IoT systems[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], resulting in superficial implementation of transformation measures.Conversely, when digitalization exceeds a critical threshold, technologies such as intelligent sensors, big data platforms, and cloud computing enable comprehensive dynamic monitoring of pollution sources[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].This facilitates precise identification of high-pollution processes and optimization of emission reduction strategies, significantly amplifying the marginal health benefits of MGT.Moreover, digitalization enhances \"government-enterprise-public\" collaborative governance efficacy:Low-digitalization phase: Reliance on manual government inspections, delayed corporate environmental disclosures, and restricted public participation channels collectively impair policy implementation efficiency[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].Post-threshold phase: Blockchain ensures carbon data transparency and traceability[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], digital government platforms enable real-time pollution data disclosure, and social media empowers public supervision, collectively compelling substantive green transformation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Therefore, we propose:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003e(H3)\u003c/b\u003e: The impact of manufacturing green transformation on public health exhibits a digitalization-driven threshold effect, becoming significantly positive only when digitalization surpasses a critical level.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3.Study Design","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Model Specification\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Baseline Regression Model\u003c/h2\u003e\u003cp\u003eTo examine the direct impact of manufacturing green transformation (MGT) on public health, we establish the following fixed-effects model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Public}\\text{}\\text{Health}}_{\\text{it}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}{\\text{\u0026beta;}}_{\\text{1}}{\\text{Green}}_{\\text{it}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}{\\text{Controls}}_{\\text{it}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026gamma;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Public}\\text{}\\text{Health}}_{\\text{it}\\text{}}\\)\u003c/span\u003e\u003c/span\u003edenotes the health level of residents in province \u003cb\u003ei\u003c/b\u003e year \u003cb\u003et\u003c/b\u003e ,measured by population mortality rate;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Green}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e represents the level of manufacturing green transformation༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Controls}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e is a vector of control variables༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026mu;}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026gamma;}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e denote province and year fixed effects, respectively༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026epsilon;}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e is the idiosyncratic error term。\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2Mediation Effect Model\u003c/h2\u003e\u003cp\u003eTo identify transmission mechanisms:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Mediator}}_{\\text{it}}\\text{=}{\\text{\u0026alpha;}}_{\\text{0}}\\text{+}{\\text{\u0026alpha;}}_{\\text{1}}{\\text{Green}}_{\\text{it}}\\text{+}{\\text{\u0026alpha;}}_{\\text{2}}{\\text{Controls}}_{\\text{it}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026gamma;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Public}\\text{}\\text{Health}}_{\\text{it}}\\text{=}{\\text{\u0026delta;}}_{\\text{0}}\\text{+}{\\text{\u0026delta;}}_{\\text{1}}{\\text{Green}}_{\\text{it}}\\text{+}{\\text{\u0026delta;}}_{\\text{2}}{\\text{Mediator}}_{\\text{it}}\\text{+}{\\text{\u0026delta;}}_{\\text{3}}{\\text{Controls}}_{\\text{it}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026gamma;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{it}}\\text{}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mediator variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Mediator}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e captures either public environmental concern or industrial structure upgrading.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Threshold Effect Model\u003c/h2\u003e\u003cp\u003eTo test nonlinear effects:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Public}\\text{}\\text{Health}}_{\\text{it}}\\text{=}{\\lambda}_{\\text{1}}{\\text{Green}}_{\\text{it}}\\text{\u0026times;}\\text{I}\\text{（}{\\text{Digi}}_{\\text{it}}\\text{\u0026le;}\\theta\\text{）+}{\\lambda}_{\\text{2}}{\\text{Green}}_{\\text{it}}\\text{\u0026times;}\\text{I}\\text{（}{\\text{Digi}}_{\\text{it}}\\text{\u0026gt;}\\theta\\text{）+}\\text{\u0026beta;}{\\text{controls}}_{\\text{it}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026gamma;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Digi}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e is the digitalization level of manufacturing, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\)\u003c/span\u003e\u003c/span\u003e is the estimated threshold value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{I}\\text{（\u0026middot;）}\\)\u003c/span\u003e\u003c/span\u003edenotes the indicator function (1 if condition true, 0 otherwise).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Selection\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Dependent Variable\u003c/h2\u003e\u003cp\u003eResidents' health level is measured by crude death rate (\u0026permil;, per thousand population), a widely adopted indicator in public health research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Core Independent Variable\u003c/h2\u003e\u003cp\u003eManufacturing green transformation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Green}}_{\\text{it}}\\)\u003c/span\u003e\u003c/span\u003e) is quantified through a comprehensive evaluation index system constructed along five dimensions:1.Green InnovationFinancial 2.Support Resource Consumption 3.Pollution Emissions 4.Ecological Protection.The specific measurement framework with indicator weights and calculation methodology is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation Index System for Manufacturing Green Transformation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e\u003cp\u003eManufacturing Green Transformation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTier 1 Indicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTier 2 Indicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGreen Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen invention patent applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen utility model patent applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFinancial Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen credit ratio (Provincial green credit volume / Total provincial credit volume)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen bond penetration (Total green bond issuance / Total bond issuance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen fund share (Market capitalization of green funds / Total fund market capitalization)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eResource Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectricity intensity (Power consumption / Industrial value-added)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater intensity (Industrial water use / Regional GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnergy intensity (Energy consumption / Regional GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePollution Emissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCOD emission intensity (Chemical oxygen demand in wastewater / Industrial value-added)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSO₂ emission intensity (Sulfur dioxide emissions / Industrial value-added)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustrial solid waste intensity (General industrial solid waste / Industrial value-added)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEcological Protection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustrial pollution control investment ratio to GDP (Industrial pollution control investment / GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComprehensive utilization rate of industrial solid waste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnvironmental fiscal expenditure ratio (Local environmental protection expenditure / General fiscal budget expenditure)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Mediating Variables\u003c/h2\u003e\u003cp\u003ePublic environmental concern:Measured by normalizing Baidu Search Index results for the keyword \"environmental pollution\".\u003c/p\u003e\u003cp\u003eIndustrial structure upgrading:Quantified as the ratio of tertiary industry value-added to secondary industry value-added.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Control Variables\u003c/h2\u003e\u003cp\u003eTo mitigate confounding effects, we control for eight provincial-level factors:Economic development level 、Urbanization rate 、Health expenditure、Medical resources 、Elderly dependency ratio、Pension insurance coverage rate、Education attainment、Climate conditions\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.5 Threshold Variable\u003c/h2\u003e\u003cp\u003eManufacturing digitalization level - constructed through a multidimensional index system comprising:Digital infrastructure、Digital application、Digital innovation、Digital effectiveness、Complete variable definitions are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation Index System for Manufacturing Digitalization Level.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"27\" rowspan=\"28\"\u003e\u003cp\u003eManufacturing Digitalization Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTier 1 Indicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTier 2 Indicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eDigital Infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of registered domains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIPv4 address count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBroadband access ports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMobile phone penetration rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLong-distance fiber optic cable density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMobile base station density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital equipment investment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eDigital Application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigitally enabled enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWebsites per 100 enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE-commerce adoption rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital inclusive finance index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICT sector employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTelecom revenue as % of GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE-commerce turnover as % of GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComputers per 100 employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eDigital Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFull-time equivalent R\u0026amp;D personnel in industrial enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026amp;D personnel in high-tech industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026amp;D projects in industrial enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntramural R\u0026amp;D expenditure in high-tech industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026amp;D expenditure in industrial enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTechnology transaction value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGranted patents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDigital Effectiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoftware revenue as % of GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIT services revenue as % of GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh-tech industry revenue share\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRevenue per employee in high-tech industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal profits in high-tech industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definitions and Descriptive Statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariableProperty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariableName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariableDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd.Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic health level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrude death rate (\u0026permil;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManufacturing green transformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite index by TOPSIS-entropy weight method (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP per capita (CNY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrbanization rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban population / Total population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealthcare expenditure / GDP (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHospital beds per 10,000 people\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElderly dependency ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation aged\u0026thinsp;\u0026ge;\u0026thinsp;65 / Working-age population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePension insurance coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePension insurance participants / Total population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage years of schooling (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClimate conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual mean temperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMediating variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic environmental concern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaidu Search Index for \"environmental pollution\" (standardized)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e215.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial structure upgrading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertiary sector value-added / Secondary sector value-added\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreshold variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManufacturing digitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite index from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e evaluation system (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data Description\u003c/h2\u003e\u003cp\u003eThis study employs panel data covering 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan due to data limitations) from 2012 to 2022, selected based on data availability and completeness.Primary data sources include the China Statistical Yearbook, China Industrial Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Insurance Statistical Yearbook, and the China Research Data Service Platform (CNRDS).Isolated missing values were addressed through linear interpolation.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Empirical Results and Analysis","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline Regression Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Regression Results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd.Err.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing green transformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.549**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.734***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElderly dependency ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePension insurance coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimate conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.916***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.494*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: ① ** and * denote statistical significance at the 5% and 10% levels, respectively.② Robust standard errors are reported in parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study employs a two-way fixed effects model to examine the relationship between manufacturing green transformation (MGT) and public health.As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, MGT exerts a significantly positive impact on public health (5% level) in the baseline specification with only fixed effects.When control variables are introduced, this positive effect strengthens substantially, achieving statistical significance at the 1% level.This enhanced significance after accounting for socioeconomic confounders suggests that the net health benefits of MGT become more pronounced, potentially because certain controls (e.g., medical resources) inherently correlate with green transformation\u0026mdash;controlling for these variables isolates MGT's independent contribution.Notably, while medical resources demonstrate the expected positive coefficient, their effect size is considerably smaller than MGT's direct impact, implying that environmental interventions may outperform traditional healthcare inputs.The significantly positive coefficient of the elderly dependency ratio further aligns with global aging challenges, underscoring the necessity for integrated policy coordination between green transformation and pension security systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Robustness Test\u003c/h2\u003e\u003cp\u003eTo ensure the reliability of baseline regression results and precisely characterize the impact of manufacturing green transformation (MGT) on public health, robustness tests were conducted through lagged variables, truncation procedures, and exclusion of special years.First, a one-period lagged MGT index was adopted to mitigate potential reverse causality.Second, all variables underwent 1% winsorization to eliminate outlier distortion.Third, samples from 2012\u0026ndash;2019 were retained to exclude COVID-19 pandemic interference during 2020\u0026mdash;a significant public health anomaly.Results presented in Columns (1)-(3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e consistently confirm the resilience of our core findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness Test Results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaggedVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWinsorization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExclude2020\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eManufacturing green transformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.660**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.180**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLagged MGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.920***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.090**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8.690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.348)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Robust standard errors in parentheses; same for subsequent tables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe robustness tests reveal three key insights: First, when employing a one-period lagged manufacturing green transformation (MGT) variable, the coefficient magnitude increases further, suggesting persistent and potentially cumulative health benefits over time.Second, after 1% winsorization, the coefficient remains directionally consistent with baseline results but moderately attenuates, indicating limited outlier influence while highlighting the need to scrutinize region-specific dynamics in high-pollution clusters or green transformation pioneers.Third, exclusion of the COVID-19 pandemic period (2020) renders the MGT coefficient statistically insignificant though directionally positive, likely due to pandemic-induced distortions in health data\u0026mdash;including healthcare system disruptions and mortality reporting anomalies\u0026mdash;which may obscure long-term environmental health effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Mediation Effect Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Effect Results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePublicEnv.ConcernPathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eIndustrialUpgradingPathway\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)Publicenvironmentalconcern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)DeathRate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)Industrialstructureupgrading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)DeathRate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eManufacturing green transformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.194**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.637**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.296***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.488*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.284)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.277)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePublic environmental concern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.500*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndustrial structure upgrading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.829***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.214)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.436***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.978***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.305)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(7.201)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study examines the dual mediating pathways proposed in Hypothesis H2\u0026mdash;where public environmental concern and industrial structure upgrading transmit the health effects of manufacturing green transformation (MGT)\u0026mdash;using a formal mediation model.Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e results demonstrate that MGT significantly enhances public environmental concern (coeff.= 0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Column 1), which subsequently reduces mortality rates (coeff.= -0.500, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; Column 2), confirming its mediating role through heightened environmental awareness that motivates individual pollution-avoidance behaviors and community-led emission monitoring.Simultaneously, MGT drives substantial industrial restructuring (coeff.= 0.296, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Column 3), with upgrading significantly lowering mortality (coeff.= -0.829, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Column 4) by transitioning high-pollution manufacturing to cleaner service sectors\u0026mdash;reducing industrial emissions while improving health behaviors through service-sector employment growth.These robust dual-pathway mechanisms collectively validate H2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Threshold Effect Analysis\u003c/h2\u003e\u003cp\u003eThis study employs a bootstrap test with 500 replications to examine the threshold effect of manufacturing digitalization on the health impact of green transformation.As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, a single-threshold model demonstrates statistical significance (F\u0026thinsp;=\u0026thinsp;23.59, p\u0026thinsp;=\u0026thinsp;0.080) at the 10% level, while the double-threshold model fails to reject the null hypothesis (p\u0026thinsp;=\u0026thinsp;0.916).These results confirm the existence of a statistically significant single threshold effect in the relationship between manufacturing digitalization and the health returns from green transformation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold Effect Test.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBootstrapReps\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1%CriticalValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5%CriticalValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10%CriticalValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.59*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSingle Threshold Model Results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFertilizerapplication\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.Err.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing digitalization\u0026thinsp;\u0026le;\u0026thinsp;0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing digitalization\u0026thinsp;\u0026gt;\u0026thinsp;0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.687**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIncluded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBuilding upon the threshold analysis, we validated the model by incorporating the manufacturing digitalization threshold (0.343) and corresponding coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).Results reveal a structural shift in green transformation's health impact when digitalization exceeds this critical value: Below the threshold (\u0026le;\u0026thinsp;0.343), green transformation shows an insignificant negative coefficient (β=-0.242, p\u0026thinsp;\u0026gt;\u0026thinsp;0.10), indicating limited health benefits due to technological immaturity, monitoring deficiencies, and implementation costs that constrain pollution reduction efficacy.Conversely, above-threshold digitalization (\u0026gt;\u0026thinsp;0.343) strengthens the effect substantially (β=-1.687, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), where intelligent monitoring and clean production optimization enhance environmental governance efficiency, significantly curbing hazardous emissions and amplifying health gains through reduced pollution exposure.\u003c/p\u003e\u003cp\u003eConsequently, the threshold analysis confirms a statistically significant digitalization threshold governing manufacturing green transformation's impact on public health.Below this critical value, health improvements remain statistically insignificant; only when digitalization surpasses the threshold does green transformation yield significant health improvements.These findings conclusively validate Hypothesis H3.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions and Implications","content":"\u003cp\u003eUsing provincial panel data from China (2012\u0026ndash;2022), this study systematically examines manufacturing green transformation's impact on public health and its transmission mechanisms.Key conclusions emerge:\u003c/p\u003e\u003cp\u003e(1)Manufacturing green transformation significantly improves public health outcomes.\u003c/p\u003e\u003cp\u003e(2)This health impact operates through dual mediating channels: public environmental concern and industrial structure upgrading.\u003c/p\u003e\u003cp\u003e(3)A significant digitalization threshold governs the health effects, with impacts becoming statistically meaningful only above critical digital capability levels.\u003c/p\u003e\u003cp\u003eCorresponding policy implications follow:\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Deepening Green-Digital Integration in Manufacturing to Unleash Synergistic Governance Effects\u003c/h2\u003e\u003cp\u003eThis integration serves as a critical pathway for enhancing environmental governance efficacy.Policymakers should strengthen top-level design by incorporating both priorities into a unified policy framework, implementing a \"digitalization-empowered green transformation\" development strategy.Institutional mechanisms for cross-departmental coordination must be established, integrating resources from industrial, environmental, and technological sectors to form policy coherence.Accelerate deployment of frontier technologies\u0026mdash;including IoT, big data, and AI\u0026mdash;for real-time pollution monitoring and emission reduction.Infrastructure development in central and western regions requires prioritization through 5G network expansion and industrial internet platforms to bridge regional technological disparities.Furthermore, establish eastern-developed-region knowledge transfer programs featuring technical assistance and shared digital governance frameworks, enabling less-developed regions to surpass digitalization thresholds and fully realize green transformation's health co-benefits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Accelerating Industrial Restructuring to Build a Green Low-Carbon Industrial Ecosystem\u003c/h2\u003e\u003cp\u003eIndustrial restructuring constitutes the fundamental pathway for achieving coordinated development between manufacturing green transformation and public health.Market-based mechanisms and administrative measures should be synergistically deployed to facilitate orderly phase-outs of energy-intensive and high-pollution industries, thereby creating development space for green emerging sectors.Concurrently, traditional manufacturing must be steered toward high-end, intelligent, and eco-friendly production through enterprise adoption of digital-green technologies, enhancing both operational efficiency and environmental performance.Strengthening industrial chain collaboration is critical\u0026mdash;building vertically integrated green supply chains will reduce aggregate pollution intensity via technological spillovers and economies of scale, enabling simultaneous optimization of industrial and energy structures.Complementary to these efforts, expanding green financial instruments (including credit facilities and bond markets) must strategically direct social capital toward renewable energy, environmental technologies, and advanced equipment manufacturing sectors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Strengthening Public Participation Mechanisms to Activate Societal Co-Governance in Environmental Management\u003c/h2\u003e\u003cp\u003ePublic environmental awareness serves as a critical catalyst for activating the intrinsic drivers of environmental governance.Multi-tiered public participation platforms should be established alongside enhanced environmental information disclosure systems, disseminating real-time air/water quality data and health risk alerts through official applications and social media to elevate ecological literacy.Implementing incentivized reporting mechanisms for environmental violations will empower citizen oversight of corporate emissions, creating a responsive \"public supervision-rapid action\" governance loop.Concurrently, green consumption must be promoted through carbon credit systems and eco-certification incentives, transforming consumer preferences into market pressure for corporate environmental upgrades.Complementary environmental education initiatives\u0026mdash;integrating community workshops and K-12 curricula\u0026mdash;will cultivate lasting ecological consciousness, ultimately forging a sustainable governance ecosystem characterized by governmental leadership, corporate accountability, and civic engagement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Implementing Regionally Differentiated Policies to Overcome Digitalization Threshold Constraints in Central-Western China\u003c/h2\u003e\u003cp\u003eAddressing the digital infrastructure deficits and delayed green transition in central-western China requires implementing a regionally differentiated strategy of \"precision policymaking and phased advancement.\" Targeted fiscal interventions should prioritize industrial internet deployment and smart environmental monitoring platforms to reduce corporate digitalization costs.The innovative \"flying land economy\" model warrants expansion\u0026mdash;integrating eastern technological capabilities with western renewable resources, exemplified by deploying intelligent operations systems in western photovoltaic bases to optimize clean energy utilization.Concurrently, enhanced interregional ecological compensation mechanisms must be established, utilizing carbon trading markets and horizontal fiscal transfers to balance developmental equities and ensure equitable access to sustainability dividends.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eInformed Consent Statement\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research was funded by National Social Science Fund Project,grant number 19BTJ039.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eX. Li: Conceptualization, Funding acquisition, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.Y. Deng: Methodology, Software, Investigation, Data curation, Formal analysis, Visualization.All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003ehe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXi, J. \u003cem\u003eHold High the Great Banner of Socialism with Chinese Characteristics and Strive in Unity for the Comprehensive Construction of a Modern Socialist Country: Report at the 20th National Congress of the Communist Party of China\u003c/em\u003e (.M.People's Publishing House, 2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, L., Huang, S. \u0026amp; Zhang T.Research on green production efficiency and health efficiency in industry and agriculture: Based on a parallel two-stage SBM-DEA model. \u003cem\u003eJ. Econ. 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Forum\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e (4), 94\u0026ndash;110 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Manufacturing green transformation, Public health, Mediating effect, Threshold effect","lastPublishedDoi":"10.21203/rs.3.rs-7615650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7615650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study utilizes panel data from 30 provinces in China spanning the period 2012 to 2022 to investigate the impact of transformation (MGT) on public health.The findings reveal that: (1) Manufacturing greening significantly enhances public health levels and is associated with a notable reduction in population mortality rates.(2) Increased public environmental awareness and industrial structure upgrading serve as the primary mechanisms through which manufacturing greening improves public health.(3) Threshold regression analysis demonstrates that the positive effect of manufacturing greening on public health is significant only when the level of digitalization within the manufacturing sector exceeds a critical threshold; below this threshold, the impact is statistically insignificant.Based on these results, this paper proposes policy recommendations focusing on four key areas: fostering deep integration between greening and digitalization in manufacturing, accelerating industrial structure optimization and upgrading, enhancing public participation mechanisms, and implementing regionally differentiated policies.\u003c/p\u003e","manuscriptTitle":"The Impact of Manufacturing Green Transformation on Public Health: Mechanisms and Empirical Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 20:05:47","doi":"10.21203/rs.3.rs-7615650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T15:50:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T10:57:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T11:14:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T14:56:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111313129930257740425925994964114388090","date":"2025-09-29T14:44:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79815081965247129623834797774793117896","date":"2025-09-29T14:24:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"821614623355225239429304801629910585","date":"2025-09-29T12:37:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147933587442564510513709971007238287085","date":"2025-09-24T11:18:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T10:48:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T10:46:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T19:16:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T10:58:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-18T08:55:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1605bdc-ce12-428d-98ba-197b5d94c462","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55765651,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55765652,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":55765653,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2026-02-02T16:08:27+00:00","versionOfRecord":{"articleIdentity":"rs-7615650","link":"https://doi.org/10.1038/s41598-026-37563-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-28 15:58:47","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-10-07 20:05:47","video":"","vorDoi":"10.1038/s41598-026-37563-2","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37563-2","workflowStages":[]},"version":"v1","identity":"rs-7615650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7615650","identity":"rs-7615650","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}