Introduction

In recent years, Environmental, Social, and Governance (ESG) considerations have emerged as a cornerstone of corporate governance and global sustainability agendas. Driven by the United Nations’ Sustainable Development Goals (SDGs), stakeholders—including investors, regulators, and consumers—are increasingly demanding accountability for environmental and social outcomes1. Bloomberg Intelligence projects ESG investments to reach $40 trillion by 2030, underscoring their growing influence on capital allocation and corporate behavior. This trend is particularly important for China, as its powerful manufacturing industry faces growing pressure to align itself with green transition goals. Under the “Made in China 2025” strategy, the government has promoted smart manufacturing through industrial policy, with industrial robots playing a central role in improving efficiency, precision, and sustainability. In 2021 alone, China accounted for nearly half of global industrial robot installations (The International Federation of Robotics, 2022), reflecting both market expansion and strong policy guidance. These developments occur within a distinctive institutional context—characterized by centralized planning, performance-driven local governance, evolving ESG disclosure rules, and market pressures—which creates a unique environment in which ESG may shape firms’ technology adoption behavior more directly than in other economies.

Despite growing academic interest in ESG, previous research has primarily focused on its impact on internal governance mechanisms such as board structure and executive incentives2,3,4, as well as sustainability outcomes such as carbon emissions and renewable energy5,6,7. A few studies have begun to explore ESG as a potential driver of technological transformation8,9,10, but most of the focus has been on green technology innovation(W.11,12,13,14,15), and digital technologies(J.11,12,13,16,17). In contrast, the link between ESG performance and the adoption of industrial robots—especially in the context of emerging markets—has been largely unexplored. This gap is critical because industrial robots not only enhance productivity through automation but also contribute to environmental goals through precise control and waste reduction.

It is worth noting that ESG is rooted in the concept of corporate development, and existing research typically analyzes it through the lens of stakeholder theory18,19,20. However, when exploring the relationship between ESG and industrial robots, it is essential to consider the fact that industrial robots serve as tools for companies to upgrade industrial technology in response to societal changes21. The TOE (Technology-Organization-Environment) framework offers a more comprehensive perspective on the multidimensional factors involved in corporate technology adoption22, focusing on internal drivers such as technological readiness or organizational capabilities. It is important to note that this study does not simply map ESG onto existing TOE dimensions but instead views ESG as a cross-cutting force that reconfigures firms’ strategic responses to external environments, governance, and social challenges. This theoretical integration allows us to examine not only direct effects but also intermediary mechanisms and heterogeneous effects across different firm types. Therefore, this study aims to analyze panel data from Chinese listed manufacturing companies (2009–2021) using an empirical model based on the TOE framework to uncover the direct impact, indirect mechanisms, and heterogeneous effects of ESG on the industrial robot applications in the manufacturing sector. The key contribution of this study is that it not only fills the research gap between ESG ratings and industrial robot applications but also provides valuable insights for governments and businesses to formulate more effective policies and strategies by highlighting the role of ESG in driving technology adoption, particularly in accelerating the digitalization and green transformation of the manufacturing industry.

This study’s innovation lies in its application of the traditional TOE framework, which typically focuses on the impact of firm-level factors on technology adoption across the technological, organizational, and environmental dimensions. However, in the context of growing global awareness of sustainable development and the widespread use of ESG ratings, the external pressures on firms are no longer limited to technological or economic factors. They also encompass social responsibility, environmental protection, and governance transparency. Therefore, this study extends the TOE framework by integrating ESG factors, broadening its scope to account for industrial robot applications. This expanded framework provides a more comprehensive perspective on firms’ decision-making and behaviors in the face of transformative technology adoption, revealing that ESG is not only a tool for corporate sustainability strategies but also a key driver of new technology adoption.

Theoretical analysis and research hypothesis

Direct impact

The TOE framework, which emphasizes the interaction between technological readiness, organizational capabilities, and external environmental pressures, serves as a robust foundation for examining corporate adoption of emerging technologies. However, traditional TOE studies have largely focused on technology adoption driven by cost–benefit analyses, often overlooking the role of sustainability considerations in the era of ESG integration. As global attention on sustainable development increases, ESG ratings have become a strategic tool, shifting corporate priorities from short-term profitability to long-term sustainability. ESG thus functions as both a compliance measure and a driver of technological innovation, aligning environmental, social, and governance objectives with firms’ operational strategies. This alignment is particularly evident in the manufacturing sector, where industrial robots, as solutions for both efficiency enhancement and environmental compliance, have become key to achieving ESG goals.

Within the TOE framework, the technological dimension reflects how ESG-driven environmental mandates, such as carbon neutrality goals and energy regulations, create pressure on firms to adopt advanced technologies23,24 . Under these mandates, firms must reduce carbon footprints and enhance resource efficiency. Industrial robots, with their precision and automation capabilities, help meet these challenges by reducing energy consumption, minimizing material waste, and optimizing production processes, thus aligning with ESG’s environmental criteria. This direct connection between ESG policies and technology adoption underscores the strategic role of industrial robots in achieving sustainability objectives. Therefore, we hypothesize:

H1: ESG promotes industrial robot applications in manufacturing enterprises.

Indirect mechanisms

ESG promotes industrial robot applications in manufacturing firms through the three key dimensions of the TOE framework—enhancing technology adoption, improving organizational governance, and responding to changes in the external environment (Fig. 1). Specifically:

Fig. 1
figure 1

Indirect transmission mechanism.

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Technological dimension: ESG redefines innovation imperatives

In traditional TOE models, the technological dimension reflects a firm’s perception of a technology’s relative advantage, compatibility, and complexity. ESG deepens this logic by restructuring the criteria of innovation value. Under ESG mandates, firms must adopt technologies that reduce carbon footprints, track emissions, and improve ecological efficiency—not merely those that optimize costs or productivity25. This reframes industrial robots as compliance-aligned innovations: their precision, energy efficiency, and ability to minimize waste directly serve ESG-driven environmental targets. Furthermore, ESG frameworks introduce forward-looking institutional pressures that convert environmental compliance from optional to obligatory. This reorients the firm’s technological strategy toward low-impact and automation-intensive solutions, positioning industrial robots as critical to long-term innovation legitimacy.

Organizational dimension: ESG institutionalizes governance-driven change

The organizational component of TOE traditionally considers a firm’s internal resources and leadership support for innovation. ESG transforms this dimension by embedding new governance norms—such as transparency, stakeholder responsiveness, and non-financial accountability—into firm-level management systems. These requirements often give rise to internal monitoring mechanisms (e.g., ESG committees, sustainability KPIs, ethical auditing systems) that require high levels of traceability, data visibility, and process standardization. Industrial robots contribute to these goals by enabling real-time monitoring, reducing manual errors, and improving reporting accuracy. ESG thereby shifts the organizational logic from resource sufficiency toward governance rationalization, making automation not just operationally beneficial but also essential for internal accountability and external credibility.

Environmental dimension: ESG converts market and regulatory pressures into institutional mandates

In the TOE framework, the environmental dimension captures external forces such as competition, regulation, and technological trends. ESG amplifies and formalizes these forces by turning them into codified institutional pressures. ESG scoring systems, sustainability-linked finance, supply chain audits, and mandatory ESG disclosures all heighten the institutional weight of environmental performance. This creates a shift from market-driven incentives to norm-based conformity pressures. For manufacturers, industrial robots offer a pathway to meet these formalized demands—by reducing emissions, satisfying safety protocols, and facilitating green certifications. ESG thus redefines the external environment as a space of institutionalized expectations, in which non-compliance can mean financial penalties, reputational damage, or market exclusion.

In summary, this paper proposes the following hypotheses:

H2: ESG promotes industrial robot applications in manufacturing enterprises by enhancing technology adoption.

H3: ESG promotes industrial robot applications in manufacturing enterprises by improving organizational governance.

H4: ESG promotes industrial robot applications in manufacturing enterprises by responding to changes in the external environment.

Methodology and data

Model specification

To examine the impact of ESG on industrial robot applications in manufacturing firms, we constructed a regression model (1) using the difference-in-differences (DID) method. In the model, \(irp\) represents the level of industrial robot applications, while \(esg\) is a dummy variable indicating whether a firm posts ESG, varying by individual treatment groups and treatment periods, and capturing the average effect of ESG reporting. \(X_{it}\) includes the selected control variables(\(tobinq\),\(fixed\),\(growth\),\(assetgrowth\),\(profit\),\(roa\)), \(\gamma_{i}\) represents time-fixed effects, \(\vartheta_{t}\) represents individual-fixed effects that remain constant over time, and \(\varepsilon_{i,t}\) is the random disturbance term. The \(X_{it}\), \(\gamma_{i}\), \(\varepsilon_{i,t}\) same below.

$$irp_{i,t} = a_{0} + a_{1} esg_{i,t} + a_{2} X_{i,t} + \gamma_{i} + \vartheta_{t} + \varepsilon_{i,t}$$
(1)

The DID model needs to satisfy the parallel trend assumption, i.e., the trend of industrial robot applications in manufacturing firms must be parallel for both published and unpublished firms before the release of the ESG. Therefore, this study adopts the event study method to conduct the parallel trend test, which is expressed by the formula:

$$irp_{i,t} = \alpha_{0} + \sum\limits_{k \ge - 4,k \ne - 1}^{7} {\alpha_{k} D_{i,t}^{k} } + X_{i,t} + \gamma_{i} + \vartheta_{t} + \varepsilon_{i,t}$$
(2)

where \(\alpha\) denotes a set of dummy variables, \(D_{i,t}^{k}\) means that when firms publish ESG reports in the year and after the year takes the value of 1, and before the publication takes the value of 0, and the rest of the variables have the same meaning as in Eq. (2). In the regression analysis of the parallel trend test, this paper takes \(k\) = -1, i.e. 1 year before the policy implementation, as the base period, so the dummy variable \(D_{i,t}^{ - 1}\) C is not included in Eq. (2). Finally, the coefficients of \(\alpha_{k}\) can be tested to determine whether the requirements of the parallel trend test are met.

Description of variables and data sources

Explained variable

The core variable in this study is the level of enterprise robotics adoption. The enterprise-level robot penetration indicator is constructed based on the methodology outlined by Lee et al.26. The conceptualization of firm-level robot penetration follows a similar approach to the Bartik Instrumental Variable (BIV). Specifically, in the first step, industry-level robot penetration is calculated:

$$IndusRobot_{i,t} = \frac{{MR_{i,t} }}{{L_{i,t} = 2009}}$$
(3)

where \(MR_{i,t}\) denotes the stock of industrial robots in industry \(i\) in year \(t\), ‘\(L_{i,t}\) = 2009’ denotes the number of people employed in industry \(i\) in 2009 (the base period), and \(IndusRobot_{i,t}\) denotes the level of industrial robot applications in industry \(i\) in year \(t\). In the second step, the enterprise-level robot application-level indicator is constructed:

$$ipr_{j,i,t} = \frac{{PWP_{j,i,t = 2010} }}{{PWP\_median_{t = 2010} }}x\frac{{MR_{i,t} }}{{L_{i,t} = 2009}}$$
(4)

where \(\frac{{PWP_{j,i,t = 2010} }}{{PWP\_median_{t = 2010} }}\) denotes the employment structure of firm \(j\) in industry \(i\) in the manufacturing industry in 2011, expressed as the ratio of the number of production staff share to the median of the production staff share of all firms in the manufacturing industry in 2011. The level of industrial robot applications in the manufacturing industry is matched to the micro level of listed companies by the above formula.

To enhance understanding of the firm-level robot penetration variable, we illustrate the calculation with a simplified example.

Suppose industry A had a stock of 10,000 robots in 2016, and its employment in the base year 2009 was 2,000,000. The robot penetration for industry A in 2016 is therefore:

$$IndusRobot_{A,2016} = \frac{10000}{{2000000}} = 0.005$$
(5)

Assume that firm X operates 100% in industry A, so its weighted industry robot exposure is also 0.005.

In 2016, firm X’s production staff accounted for 60% of total employees, while the median production staff share among all manufacturing firms in 2016 was 40%. Then the firm’s employment structure adjustment factor is:

$$\frac{{PWP_{X,A,t = 2016} }}{{PWP\_median_{t = 2016} }} = \frac{0.6}{{0.4}} = 1.5$$
(6)

Therefore, the firm-level robot penetration for firm X in 2016 is:

$$ipr_{X,A,t = 2016} = 0.005*1.5 = 0.0075$$
(7)

Core explanatory variable

The core explanatory variable in this study is ESG ratings (\(esg\)). Existing ESG rating agencies differ in the construction of indicators, data sources, and scoring criteria, resulting in the ESG scores of the same company being significantly differentiated across rating systems. While continuous scoring may introduce measurement errors due to inconsistent criteria, binary dummy variables can more robustly capture market signals of ESG management awareness by identifying firms’ decision-making behavior of “whether to proactively disclose ratings”. In this study, ESG rating disclosures are mainly concentrated in 2015, 2018, and 2019, with 2015 selected as the benchmark year due to a notable increase in standardization and disclosure activity. Drawing on the approach of Yang et al.10 and others, the assessment focuses on the timing of the announcement of ESG ratings by listed manufacturing firms. In this approach, if a manufacturing firm discloses its ESG rating at the initial stage, the firm’s ESG rating value at that stage and all subsequent stages is 1. This categorization process produces a dataset with 604 manufacturing firms in the control group and 182 in the experimental group.

Mechanism variables

This paper investigates the mechanisms through which ESG influences the application of industrial robots in manufacturing firms from three key dimensions—technology, organization, and environment—based on the TOE framework. The variables used to study these mechanisms include: green technology innovation (\(green\)) measured by the number of green invention patent applications, firms’ investment (\(cp\)) measured by the ratio of capital expenditure to total assets, firms’ environmental awareness (\(ea\)) measured by the frequency of environmental protection terms in the company’s annual report, firms’ financing constraints (\(sa\)) measured by the SA index based on firm size and age, environmental regulation (\(er\)) measured by the frequency of environmental protection terms in government reports, and environmental subsidies (\(subsidy\)) measured by the ratio of environmental subsidies received to total assets.

Control variables

The control variables selected for this paper and their rationale are as follows: the Tobin’s Q (\(tobinq\)) reflects a firm’s investment opportunities and can measure the market’s expectations of the firm’s future growth27; the fixed asset ratio (\(fixed\)) reflects a firm’s long-term capital structure and affects its decision to make long-term investments, such as in industrial robots28; the growth rate of operating revenue (\(growth\)) measures a firm’s market expansion and product competitiveness, and may affect its investment in technological innovations29; the growth rate of total assets (\(assetgrowth\)) shows the the speed of a firm’s scale expansion, which affects its ability to invest in capital and technology; gross sales margin reflects a firm’s profitability and cost control (\(profit\)), which may affect its technology adoption in ESG practices30,and net profit margin on total assets (\(roa\)) measures a firm’s ability to use its assets to generate profits, which affects its potential to invest in new technologies31. These control variables help to exclude the interference of other factors and ensure that the study focuses on the relationship between ESG and industrial robot penetration. By incorporating these variables, the model accounts for firms’ heterogeneity in financial structure, growth dynamics, and operational efficiency, which are essential determinants of capital-intensive technological adoption. This selection ensures that any observed effects attributed to ESG performance are not confounded by baseline differences in firms’ investment capacity or market conditions, thereby enhancing the internal validity of the study’s causal inference.

Data sources

The screening process for listed manufacturing companies involved the following steps: First, ST-listed companies were excluded. Second, companies listed in the current year were excluded due to their shorter listing period, insufficient historical information, and significant differences in information disclosure compared to other firms. After applying these criteria, the final sample included 604 listed manufacturing companies. Considering data availability, the selected time period for analysis was 2009–2022. Measurement data for relevant indicators of listed manufacturing companies were obtained from the CSMAR database, while ESG ratings and corporate employee composition data were sourced from the WIND database. Industry-level data were drawn from the China Industrial Economic Statistics Yearbook, and city-level data were extracted from the China Urban Statistics Yearbook. The descriptive statistics for the variables are presented in Table 1.

Table 1 Descriptive statistics results of variables.
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Results

Benchmark regression results

The baseline regression results show the following sequential changes (Shown in Table 2): First, without the inclusion of control variables or double fixed effects, the effect of ESG on industrial robot applications in manufacturing firms is 0.248 and statistically significant (p < 0.01). After adding control variables, the regression coefficient remains at 0.248, indicating that the effect of ESG on adoption remains stable when accounting for other factors. Finally, when both control variables and double fixed effects are included, the regression coefficient decreases to 0.100, but it remains significant (p < 0.01). These findings suggest a positive correlation between ESG and industrial robot applications in manufacturing firms.

Table 2 Benchmark regression results.
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Parallel trend test

To ensure the validity of the Difference-in-Differences (DID) approach, this study conducts a parallel trend test to verify the key identifying assumption that, in the absence of ESG disclosures, the treatment and control groups would have followed similar trends over time. Specifically, we construct an event study framework by including a series of leads and lags of the treatment variable. Considering the data for the first four years prior to policy implementation, we define four pre-treatment periods, while the post-treatment period extends up to six years given the sample window from 2009 to 2022 and the widespread emergence of ESG disclosures in 2015. The year of ESG disclosure is treated as the baseline (omitted) period. Figure 2 presents the estimated dynamic treatment effects. The coefficients for all pre-treatment periods are statistically insignificant, suggesting that prior to ESG disclosure, the treatment and control groups did not exhibit systematic differences in trends related to industrial robot adoption. This supports the validity of the parallel trend assumption. The significant and increasingly positive coefficients in the post-treatment periods further reinforce the robustness of the estimated treatment effect.

Fig. 2
figure 2

Parallel trend test.

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Placebo test

Time-placebo test

To ensure that the observed difference in industrial robot applications between the treatment and control groups is not driven by time, this study adjusts the policy implementation timeline by 1 to 5 years, based on the parallel trends test results. A virtual policy shock is then created, and regression analysis is performed using Eq. (1). The results, shown in Fig. 3, indicate that the time-placebo test reveals no significant average treatment effect, suggesting that there is no notable difference in the time trends of ESG report releases between the treatment and control groups.

Fig. 3
figure 3

Placebo test.

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Individual placebo test

This study employs individual placebo tests to assess the robustness of the effect of ESG release on manufacturing firms. Specifically, we examine the significance of ESG by applying a placebo intervention to a sample of firms in years when ESG reports were not released. The results from the individual placebo test show that the effect of ESG on industrial robot applications is not significant in years when ESG reports were absent, as depicted in Fig. 3. This finding suggests that the observed ESG effect is not driven by unobserved factors or time trends, but rather by the actual release of ESG reports. Therefore, the individual placebo test supports our interpretation of the empirical results.

Mixed placebo test

Mixed placebo tests involve the random assignment of time and individual factors. As shown in Fig. 3, the mixed placebo test displays the kernel density distribution of the coefficients after 1,000 random groupings. The distribution approximates a normal distribution centered around 0, with most coefficients falling to the left of the baseline regression coefficients. This suggests a lack of statistically significant correlation between the coefficients of the randomized groupings, thereby confirming the robustness of the conclusions drawn from the baseline regression.

Robustness test

Propensity score matching

In this study, the propensity score matching (PSM) method is used to construct the sample. After matching, the distribution of variables becomes more balanced between the treatment and control groups, with the deviation proportion below 10%. The mean values of the variables do not show significant differences between the two groups. As shown in Table 3, the empirical results derived from the Propensity Score Matching Double Difference Method (PSM-DID), using kernel matching, caliper matching, and nearest neighbor matching techniques, indicate that ESG enhances industrial robot applications in manufacturing firms. These results demonstrate the effectiveness of ESG and confirm the robustness of the DID method.

Table 3 Robustness test.
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Lag processing

To ensure the robustness of the results, lag processing was applied to the industrial robot applications data of manufacturing firms. The regression results show that the effect of ESG on industrial robot applications is 0.061, which is statistically significant (p < 0.05). This suggests that ESG continues to positively impact robot adoption, even after accounting for the lagged effects. However, the effect is somewhat attenuated compared to the higher coefficient observed in the baseline regression.

Sample exclusion

To further verify the robustness of the results, the sample was restricted by excluding municipalities. After excluding the municipal sample, the regression results show that the effect of ESG on industrial robot applications in manufacturing firms is 0.120, and the coefficient remains statistically significant (p < 0.01). This indicates that even after excluding municipal enterprises, ESG continues to have a positive and significant impact on industrial robot applications in manufacturing firms.

Replace explanatory variables

To address the concern that the binary ESG disclosure dummy may oversimplify firms’ ESG performance and ignore heterogeneity in ESG quality, we conduct a robustness check by replacing the binary treatment variable with continuous ESG scores provided by Huazheng Index Information Service. Compared to simple disclosure indicators, the Huazheng ESG rating reflects the relative quality of firms’ ESG performance based on a standardized scoring system widely used in China’s capital market. This allows us to capture not only whether a firm discloses ESG information, but also the intensity and credibility of its ESG practices. The results (see Table 3) remain consistent with our main findings: the effect of ESG on industrial robot applications in manufacturing firms is 0.106, and the coefficient remains statistically significant (p < 0.05). This robustness check mitigates the concern of potential measurement error arising from the binary specification, and confirms that our findings are not sensitive to alternative measurements of ESG practices.

Endogenous testing

This study employs the shareholding proportion of pan-ESG funds as an instrumental variable (IV) to address potential endogenous self-selection biases in firms’ voluntary ESG disclosure decisions10. ESG funds operate under stringent green investment mandates, wherein their portfolio allocations inherently depend on firms’ compliance with standardized ESG certification requirements. This institutional characteristic creates exogenous pressure for invested firms to participate in ESG rating disclosures, as fund managers systematically prioritize issuers with verifiable sustainability credentials. Empirical evidence indicates that a one-percentage-point increase in pan-ESG fund ownership raises the probability of manufacturing firms initiating ESG rating procedures by 4.2 percentage points in the subsequent year, demonstrating the IV’s strong relevance. Furthermore, Hausman test results confirm that fluctuations in fund holdings lose explanatory power for stock price volatility after controlling for ESG disclosure status, thereby supporting the exclusion restriction. The regression results show in Table 3, line 6, and the coefficient remains statistically significant.

Mechanism testing

Given the ongoing debates surrounding traditional approaches to mechanism testing in existing literature, this study adopts a hybrid causal inference framework following the methodology of32, employing the Difference-in-Differences (DID) model for benchmark regression and double machine learning models as the tool for mechanism analysis. The results of this study demonstrate that the release of ESG has significantly contributed to industrial robot applications in manufacturing firms. This section, guided by the TOE framework, explores the mechanisms through which ESG influences industrial robot applications in manufacturing firms, focusing on three key dimensions: technology, organization, and environment. This study employs the research methodology of10, which uses a dual machine learning model to reveal the complex mechanisms through which ESG affects industrial robot applications. Table 4 presents the detailed results of this analysis, highlighting the specific ways in which each dimension contributes to the observed impacts.

Table 4 Mechanism test results.
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Enhancing technology adoption

The technology dimension, including green technology innovation and firm R&D investment, shows significant positive direct effects. These findings indicate that ESG positively influences industrial robot applications in manufacturing firms through technological factors. The positive effects of green technology innovation and R&D investment suggest that ESG encourages firms to prioritize sustainable and advanced technologies. This emphasis likely stems from regulatory incentives and stakeholder expectations for reduced environmental impact and improved efficiency. Moreover, R&D investment enhances firms’ capacity to integrate industrial robots by fostering technological readiness and innovation. ESG policies may act as a catalyst, aligning long-term sustainability goals with technological progress, thereby reducing operational risks and opening pathways for competitive advantage in manufacturing.

Improving organizational governance

The organizational dimension, encompassing corporate green awareness and financing constraints, demonstrates significant positive direct and indirect effects. These results suggest that ESG positively impacts industrial robot applications in manufacturing firms by improving organizational governance. The significant positive direct and indirect effects of corporate green awareness and financing constraints indicate that ESG can enhance organizational governance, which in turn facilitates the adoption of industrial robots. Corporate green awareness, driven by ESG factors, encourages a culture of sustainability within the firm, fostering long-term strategic planning and decision-making that aligns with green transformation. This heightened awareness may lead to more effective management of resources, better integration of sustainable practices, and prioritization of environmentally friendly technologies such as industrial robots. Moreover, addressing financing constraints through ESG initiatives, such as access to green financing or incentives for adopting sustainable technologies, provides firms with the financial flexibility needed to invest in industrial robots. Enhanced organizational governance, strengthened by ESG, also promotes better decision-making processes, improved transparency, and a stronger commitment to long-term sustainability goals. This combination of factors creates an environment where the adoption of industrial robots becomes a strategic priority for manufacturing firms.

Responding to changes in the external environment

The environmental dimension, which includes environmental regulations and environmental subsidies, shows significant positive direct and indirect effects. These findings suggest that ESG positively influences industrial robot applications in manufacturing firms by responding to external environmental factors. The positive direct and indirect effects of environmental regulations and environmental subsidies highlight the importance of the external environment in driving the adoption of industrial robots in manufacturing firms. Environmental regulations, as part of ESG, create a compliance-driven imperative for firms to adopt cleaner and more efficient technologies, such as industrial robots, to meet regulatory standards and reduce their environmental impact. These regulations not only encourage technological upgrades but also push firms to innovate and explore alternative solutions that align with sustainable practices. Furthermore, environmental subsidies, which are often provided to firms that adopt green technologies, offer financial incentives to ease the initial costs of integrating industrial robots. These subsidies can lower the financial barriers to technology adoption, making it more attractive for firms to invest in robotics for improved operational efficiency and sustainability. By responding to these external pressures—regulations and subsidies—firms can better align their strategies with national and international sustainability goals, thus accelerating the adoption of industrial robots as part of their green transformation.

Heterogeneity test

Technological heterogeneity

The sample is divided into high-tech and non-high-tech firms to analyze the effects of technological heterogeneity. Regression analysis reveals that in high-tech firms, the release of ESG does not significantly impact industrial robot applications. However, in non-high-tech firms, the release of ESG has a positive effect on industrial robot applications (Shown in Table 5). The contrasting effects of ESG on industrial robot applications in high-tech versus non-high-tech firms highlight the role of technological readiness in the adoption of new technologies. In high-tech firms, the lack of a significant impact from ESG suggests that these firms may already possess the technological capabilities and infrastructure to integrate industrial robots, regardless of their ESG performance. High-tech firms are likely to be more advanced in their innovation and digitalization processes, with a strong focus on R&D and technology adoption. As a result, their decision to adopt industrial robots may be driven more by technological necessity and strategic priorities rather than ESG pressures or incentives.

Table 5 Heterogeneity test.
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Environmental heterogeneity

To examine environmental heterogeneity, the sample is split into high-pollution and non-high-pollution enterprises. Regression results indicate that the release of ESG positively impacts industrial robot applications in high-pollution enterprises. Conversely, in non-high-pollution enterprises, ESG release does not significantly influence industrial robot applications (Shown in Table 5). The contrasting effects of ESG on industrial robot applications in high-pollution versus non-high-pollution firms highlight the role of external environmental pressures in shaping technology adoption. For high-pollution enterprises, the positive impact of ESG suggests that these firms are more likely to respond to ESG-driven incentives, such as stricter environmental regulations, emissions reduction targets, and sustainability mandates. In highly polluting industries, ESG considerations, particularly those related to environmental performance, can create significant external pressure for firms to adopt greener and more efficient technologies, such as industrial robots, in order to meet regulatory standards and reduce their environmental footprint. These firms are likely to view industrial robots as a tool for improving operational efficiency, reducing waste, and complying with environmental regulations, thereby enhancing their overall sustainability. On the other hand, non-high-pollution firms are already less exposed to stringent environmental regulations and, as a result, may not feel the same level of urgency to adopt industrial robots due to ESG considerations. These firms may prioritize other factors, such as cost reduction, productivity improvements, or technological innovation, over environmental performance, making ESG pressures less impactful in driving robot adoption. As a result, ESG’s influence on industrial robot applications is more pronounced in high-pollution firms, where the need to comply with environmental regulations and reduce pollution acts as a key driver of technological transformation.

Discussion and conclusions

Discussion

This study advances the literature on ESG and technology adoption by integrating ESG considerations into the well-established TOE framework. Our theoretical contribution lies not in proposing a wholly new framework, but in extending the TOE model’s scope by embedding sustainability-oriented institutional and normative logics. Traditionally, TOE-based models33,34,35 emphasize techno-economic factors such as cost efficiency, technological readiness, and firm size when explaining adoption behavior. In contrast, our extension incorporates ESG-driven institutional logics that reflect the growing influence of sustainability mandates. The following analysis of the E/S/G dimensions is interpretive in nature and aims to contextualize our core findings within the broader ESG framework. While the empirical models are based on composite ESG scores, these discussions draw on theoretical and policy insights to explore the differentiated implications of environmental, social, and governance factors.

From the environmental perspective, industrial robots directly support ESG’s ecological goals by optimizing energy use and minimizing material waste through precision manufacturing. However, this environmental benefit is contingent on energy sources—robots powered by non-renewable energy may inadvertently increase carbon footprints, a paradox requiring policy attention. The social (S) dimension, often underexplored in technology adoption studies, manifests in two ways. First, robot-driven automation reshapes labor dynamics: while reducing occupational hazards in dangerous tasks, it may exacerbate skill gaps and income inequality. Our governance mechanism analysis shows that firms with strong ESG governance are more likely to invest in employee retraining, mitigating social risks36. Second, ESG-aligned firms leverage robots to enhance product safety and accessibility, aligning with SDG 914. The governance (G) dimension plays a dual role. On one hand, transparent governance structures under ESG frameworks accelerate automation investments by reducing bureaucratic delays and aligning board decisions with long-term sustainability goals37. On the other hand, over-automation may centralize decision-making power in tech teams, weakening traditional governance checks. Our findings suggest that balanced governance—integrating automation with human oversight—is critical to maintaining ESG integrity.

In sum, our integration of ESG into the TOE framework offers a more holistic understanding of technology adoption under sustainability pressures. It does not replace the traditional model, but complements and extends it by foregrounding institutional and normative factors, which are increasingly salient in the era of responsible innovation.

Conclusions

This study empirically examines the impact of ESG on industrial robot applications in China’s manufacturing sector, employing the TOE framework to uncover direct effects, indirect mechanisms, and heterogeneous outcomes. The analysis of panel data from listed firms (2009–2021) yields three key contributions. First, ESG significantly accelerates industrial robot adoption, even after addressing endogeneity and robustness checks. Second, the TOE-based mechanism analysis reveals that ESG drives robot adoption through distinct pathways: enhancing green technological innovation (technology dimension), optimizing organizational governance (organization dimension), and responding to regulatory and market pressures (environment dimension). Third, heterogeneity analysis demonstrates that ESG’s impact is more pronounced in non-high-tech and high-pollution firms, suggesting its role in bridging technological gaps and addressing environmental compliance challenges.

Theoretically, this study bridges two critical gaps in the literature. By integrating ESG into the TOE framework, we extend traditional technology adoption models that prioritize economic or technical factors, highlighting sustainability-driven motivations as a strategic imperative. Practically, these findings hold significant implications for policymakers and corporate managers. Governments should design targeted incentives, such as ESG-linked subsidies for high-pollution industries, to accelerate green automation. For non-high-tech firms, enhancing access to ESG-aligned financing can alleviate technological adoption barriers. Meanwhile, firms should leverage ESG governance reforms to build transparent decision-making structures, enabling long-term investments in automation.

Implications

Building on the empirical test results and the detailed discussion in the previous paper, this study offers three key policy recommendations and further analyzes the challenges that may arise during their implementation.

First, policymakers should strengthen the linkage between corporate ESG ratings and robotics adoption. Firms with high ESG scores should be eligible for tax incentives, subsidies, and other forms of policy support, thereby encouraging industrial robot applications and fostering productivity and sustainable development. In addition, our mechanism analysis highlights the importance of both technological and organizational channels. To reinforce these pathways, we recommend that governments support the development of internal ESG governance capabilities, such as executive accountability structures and sustainability performance evaluation systems. Simultaneously, public investment in ESG-aligned R&D programs and technical training can enhance firms’ absorptive capacity for green technologies, including automation tools like industrial robots. However, local governments may prioritize short-term economic gains over long-term sustainability, potentially undermining the effectiveness of this policy38,39.

Second, our results indicate that the positive impact of ESG performance on robot adoption is significantly stronger in non-high-tech enterprises. This suggests that such firms, often facing technical and financial constraints, are more responsive to ESG-driven external incentives. Therefore, policy efforts should prioritize providing targeted subsidies or tax incentives for ESG-compliant firms in traditional and less technologically advanced sectors. In addition, we find that high-pollution enterprises exhibit a more pronounced ESG effect on robot adoption. This underscores the role of environmental pressure as a catalyst for technological upgrading. As such, regulators should consider implementing industry-specific green financing mechanisms, ESG-based certification schemes, and environmental tax rebates to accelerate automation adoption in pollution-intensive sectors. However, in the early stages of technological adoption, these firms face increased market risks, and government support may be insufficient to mitigate market volatility40.

Finally, the policy system should be optimized to promote technology adoption within the manufacturing sector. This includes policies aimed at strengthening firms’ technology adoption capabilities, improving internal governance, and enhancing technology management. The goal is to establish a sustainable innovation ecosystem. However, increasing technology adoption is a long-term objective, and traditional manufacturing industries may resist new technologies (A.11,12,13). Business leaders, in particular, may be wary of the uncertainties associated with technological change, which could slow the pace of adoption.

Limitation

It is important to acknowledge that this study may be subject to biases stemming from cultural backgrounds and personal perspectives, which could affect the generalizability of the findings.

First, the sample is limited to listed manufacturing firms in China, which may restrict the broader applicability of the results. Future research should consider including a more diverse range of firms, such as unlisted SMEs, to enhance the generalizability and robustness of the study.

Second, while the DID model and variable selection were carefully designed, they may not account for all factors influencing ESG and industrial robot applications. For instance, potential omitted variables such as firm-level innovation capacity, regional policy heterogeneity, or managerial attitudes toward automation and sustainability could affect the observed outcomes. Future studies could expand the set of control variables to capture a fuller understanding of the underlying dynamics.

Third, ESG encompasses environmental, social, and governance dimensions, and the exploration of their integration mechanisms is crucial for certain research areas. However, due to data limitations, the ESG rating data used in this study is solely sourced from SynTao Green Finance Consulting Co. (Beijing, China). Although this data is publicly available and widely recognized, reliance on a single data provider may introduce source-specific biases, such as rating methodology differences or potential conflicts of interest. Cross-validating findings using alternative ESG datasets from other reputable agencies, such as Bloomberg, or Wind, would help improve the robustness and objectivity of future research outcomes.