Industrial robots reduce carbon emissions in manufacturing through global value chains | Scientific Reports – Nature

 

Executive Summary

This report presents an empirical analysis of the impact of industrial robot application on carbon emissions within the global manufacturing sector, framed within the context of the United Nations Sustainable Development Goals (SDGs). Industrial robots, as key enablers of intelligent transformation, are examined for their potential to advance SDG 9 (Industry, Innovation, and Infrastructure) and SDG 13 (Climate Action) by fostering green, low-carbon growth. Utilizing world input-output tables and industrial robot application data, this study analyzes the role of manufacturing’s embeddedness in the Global Value Chain (GVC).

The principal findings indicate that the application of industrial robots has a significant carbon emission reduction effect. This conclusion remains robust following causal identification using instrumental variables and multiple robustness tests. The analysis reveals two primary mechanisms driving this effect:

• Labor Factor Optimization: Through a substitution effect, industrial robots optimize the allocation of labor, enhancing productivity and contributing to SDG 8 (Decent Work and Economic Growth).
• Global Value Chain (GVC) Climbing Effect: The adoption of robotics enhances international competitive advantages and improves the division of labor status, leading to further carbon emission reductions in line with SDG 12 (Responsible Consumption and Production).

Heterogeneity analysis demonstrates that the carbon reduction effect is more pronounced in developed countries, capital-intensive industries, and sectors with a high degree of digitalization. Furthermore, by examining supply-side technology spillover through the GVC, the report clarifies the spatial characteristics of this emission reduction, highlighting the importance of international cooperation as envisioned in SDG 17 (Partnerships for the Goals). These findings provide critical policy implications for advancing global “intelligent manufacturing” and decarbonization agendas.

Introduction: Aligning Industrial Automation with Sustainable Development Goals

The global economy is navigating a new phase of development characterized by refined Global Value Chains (GVCs) and an accelerating formation of a new industrial landscape. This transformation occurs amidst the escalating challenge of global climate change, which threatens to disrupt these value chains and impede progress toward the Sustainable Development Goals. The pressure to reduce carbon emissions, a core objective of SDG 13 (Climate Action), varies significantly among nations due to their differing positions in the GVC. Developing countries, while gaining economic benefits, often bear a disproportionate environmental burden, highlighting an asymmetry that challenges the principles of sustainable development.

The rise of industrial intelligence, particularly the widespread application of industrial robots, presents a pivotal opportunity to reconcile industrial growth with environmental stewardship. As a key component of SDG 9 (Industry, Innovation, and Infrastructure), this technological advancement can reshape the global production network. This report investigates the environmental consequences of industrial robot adoption, focusing on its potential to drive progress on SDG 12 (Responsible Consumption and Production) by promoting cleaner, more efficient manufacturing processes. By integrating industrial robots, GVCs, and carbon emissions into a unified analytical framework, this study aims to provide evidence-based insights for achieving a sustainable, low-carbon global economy.

Analytical Framework and Methodology

Core Hypotheses

The report’s empirical investigation is guided by three central hypotheses that connect industrial robot application to carbon emission reduction through the lens of sustainable development.

1. Hypothesis 1: The application of industrial robots, as a form of technological innovation central to SDG 9, directly and significantly reduces carbon emissions in the manufacturing industry, thereby supporting SDG 13.
2. Hypothesis 2: Industrial robots reduce carbon emission intensity through the substitution effect of labor factors. This shift towards automation enhances productivity and resource efficiency, aligning with the objectives of SDG 8 and SDG 12.
3. Hypothesis 3: The adoption of industrial robots facilitates a “climbing effect” within the Global Value Chain. By enabling industries to move to higher value-added and cleaner production stages, this mechanism reduces overall carbon intensity, contributing to both SDG 9 and SDG 12.

Data and Variables

This study utilizes a comprehensive dataset covering 38 countries and 15 manufacturing sectors from 2000 to 2014. The data sources are as follows:

• Industrial Robot Usage: Data on the stock of industrial robots at the country-industry-year level is sourced from the International Federation of Robotics (IFR).
• Economic and Environmental Data: The World Input-Output Database (WIOD) 2016 release provides data on carbon dioxide emissions, total output, value added, employment, and intermediate consumption.
• Global Value Chain Positioning: The GVC position index is calculated based on data from the WIOD and the UIBE GVC Index database.
• Control Variables: National-level variables such as trade openness and foreign direct investment are obtained from the World Bank’s World Development Indicators (WDI).

Empirical Findings: The Role of Robotics in Achieving SDG 13

Benchmark Regression Analysis

The benchmark regression results confirm that the application of industrial robots in the global manufacturing sector significantly reduces carbon emissions. The analysis, controlling for country, industry, and year fixed effects, shows that for every 1% increase in the level of industrial robot use, carbon emissions decrease by approximately 0.02%. This finding provides strong evidence that investing in industrial automation is a viable strategy for advancing SDG 13 (Climate Action) and promoting a green industrial transition under SDG 9.

Robustness and Endogeneity Assessment

To ensure the validity of the findings, a series of robustness checks were conducted. These included replacing the dependent and independent variable measurements and introducing lagged variables to address potential reverse causality. Furthermore, an instrumental variable approach using two-stage least squares was employed to mitigate endogeneity issues. The results from these tests consistently support the core conclusion that industrial robots have a significant carbon emission reduction effect, reinforcing the reliability of the findings.

Heterogeneity Analysis: Differentiated Impacts Across Economies and Industries

The impact of industrial robots on carbon emissions is not uniform. The analysis reveals significant heterogeneity, offering nuanced insights for targeted policy-making in line with SDG 17 (Partnerships for the Goals).

• By National Development Stage: The carbon reduction effect is significantly stronger in developed countries. This is attributed to their mature industrial ecosystems, greater capacity for technological absorption, and ability to leverage robots for a full-chain low-carbon transition. In contrast, developing countries may face challenges such as carbon leakage and the “energy rebound effect,” which can weaken the emission reduction potential of robotics.
• By Industry Factor Intensity: The effect is more pronounced in capital-intensive industries (e.g., automotive manufacturing) than in labor-intensive ones. In capital-intensive sectors, robots are often embedded in high-energy processes, where their precision and efficiency yield substantial energy and material savings, directly contributing to SDG 12 (Responsible Consumption and Production).
• By Industrial Digitalization Level: The carbon reduction potential is greater in industries with a higher degree of digitalization. Digital infrastructure provides the technological foundation for robots to be integrated into smart, optimized production systems that enhance energy efficiency and reduce waste.

Mechanisms for Carbon Reduction: Pathways to Sustainable Industrialization (SDG 9 & SDG 12)

The report identifies two key mechanisms through which industrial robots contribute to decarbonization, providing a roadmap for achieving sustainable industrialization.

Labor Factor Substitution and Productivity Gains (SDG 8)

The application of industrial robots leads to a significant substitution of labor, reducing labor input per unit of output. This automation of repetitive tasks enhances overall labor productivity and allows for a more flexible and efficient allocation of production factors. By minimizing resource misallocation and improving the efficiency of production processes, this mechanism reduces unnecessary energy consumption and carbon emissions. This pathway directly supports SDG 8 (Decent Work and Economic Growth) by fostering higher productivity and creating demand for higher-skilled labor to manage and maintain automated systems.

Global Value Chain Upgrading

Industrial robots serve as a catalyst for industries to ascend the Global Value Chain. By enhancing production efficiency, reducing trade costs, and enabling technological innovation, robot adoption strengthens an industry’s international competitiveness. This allows industries to move from low-end, pollution-intensive processing and assembly stages to high-end, low-carbon activities such as R&D, design, and branding. This “climbing effect” is crucial for breaking the “low-end lock-in” dilemma faced by many industries and is a core component of achieving the goals of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production).

Spatial Spillover Effects: Fostering Global Partnerships for Climate Action (SDG 17)

The analysis was extended using a dynamic Spatial Durbin Model to account for the interconnectedness of global industries. The findings reveal significant spatial spillover effects, underscoring the importance of global cooperation as advocated by SDG 17 (Partnerships for the Goals).

• Short-Term Effects: In the short term, the rapid, large-scale introduction of robots can lead to a positive spillover effect on carbon emissions in connected industries. This is due to a scale effect where increased production efficiency in one area drives up demand and production in upstream and downstream sectors, potentially increasing their emissions temporarily.
• Long-Term Effects: In the long term, the total effect becomes significantly negative. As intelligent manufacturing matures, knowledge and technology diffuse through the GVC network. This promotes breakthrough innovations in clean production and energy efficiency across the entire value chain, leading to a net reduction in global carbon emissions. The long-term trend demonstrates that technological spillovers ultimately support a collective, global transition towards decarbonization.

These results highlight that the environmental impact of automation is not confined within national borders. A coordinated, global approach is necessary to manage short-term pressures and maximize the long-term decarbonization benefits of industrial intelligence.

Conclusion and Policy Recommendations for Sustainable Development

This report concludes that the application of industrial robots is a potent driver of carbon emission reduction in the global manufacturing sector, contributing directly to SDG 9, SDG 12, and SDG 13. The effect is realized through the optimization of labor factors and by enabling industries to ascend the Global Value Chain. However, the benefits are heterogeneously distributed, and the interconnectedness of the global economy creates complex spatial spillovers.

Based on these findings, the following policy recommendations are proposed to leverage industrial automation for the 2030 Agenda:

1. Develop Differentiated National Strategies: In line with SDG 17, nations should develop tailored roadmaps. Developed countries should pioneer next-generation, low-energy robot technologies. Developing countries must focus on building absorptive capacity and creating an innovation-friendly environment to maximize the carbon reduction benefits of automation.
2. Promote GVC Upgrading and Mitigate Carbon Leakage: Developing countries should strategically deploy industrial robots to escape “low-end lock-in” within GVCs. To counter carbon leakage, international mechanisms like carbon border adjustments should be considered, alongside robust support for green technology transfer and capacity building to prevent the formation of “pollution havens.” This fosters a more equitable and effective global approach to SDG 13.
3. Foster Deep Integration of Technology and Green Industry: Governments should incentivize the integration of industrial robots with other emerging technologies like AI to optimize energy efficiency across entire industrial ecosystems. Establishing cross-border technology transfer platforms can reduce adoption costs for developing nations, ensuring the systemic emission reduction benefits of GVC upgrading are realized globally.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on the impact of industrial robots on carbon emissions in the manufacturing sector is directly and indirectly connected to several Sustainable Development Goals (SDGs). The analysis focuses on the intersection of technological innovation, industrial development, economic growth, and environmental sustainability on a global scale.

• SDG 7: Affordable and Clean Energy

  The article discusses how industrial robots affect energy efficiency and energy consumption. It explores whether the “production technology effect” of robots leads to reduced energy use or if an “energy rebound effect” increases overall energy demand, which is central to ensuring access to clean and affordable energy.

• SDG 8: Decent Work and Economic Growth

  The study analyzes the economic consequences of robot adoption, such as improving labor productivity and enhancing total factor productivity. It also examines the “labor substitution effect,” where robots replace certain jobs, impacting employment structures and economic growth, and aims to decouple this growth from environmental degradation.

• SDG 9: Industry, Innovation, and Infrastructure

  This is a core SDG for the article. The research is centered on “industrial intelligence” (innovation) through the adoption of robots in the “manufacturing industry.” It examines how this technological upgrading contributes to “sustainable industrialization” and the development of “clean and environmentally sound technologies and industrial processes.”

• SDG 12: Responsible Consumption and Production

  The article’s focus on “green and low-carbon growth,” “clean production,” and reducing carbon emissions through more efficient manufacturing processes directly aligns with promoting sustainable production patterns. The study investigates how technology can optimize resource allocation and reduce waste, as exemplified by painting robots improving material utilization.

• SDG 13: Climate Action

  This is the most prominent SDG in the article. The entire study is framed around understanding and mitigating “global climate change” by analyzing the “carbon emission reduction effect” of industrial robots. It directly addresses the need for “decarbonization” and integrating climate change measures into industrial strategies.

• SDG 17: Partnerships for the Goals

  The analysis is conducted from the perspective of the “global value chain (GVC),” which inherently involves international trade, cooperation, and technology spillovers between developed and developing countries. The article’s policy recommendations emphasize the need for transnational mechanisms and technology transfer to achieve global climate goals, reflecting the spirit of global partnership.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s content, several specific SDG targets can be identified as directly relevant to the research questions, methodology, and findings.

1. SDG 7: Affordable and Clean Energy

   • Target 7.3: “By 2030, double the global rate of improvement in energy efficiency.” The article directly investigates this by analyzing how robots impact “energy intensity” and “energy efficiency.” It mentions that robots can reduce “ineffective energy consumption” but also acknowledges the potential for an “energy rebound effect,” which are key considerations for this target.

2. SDG 8: Decent Work and Economic Growth

   • Target 8.2: “Achieve higher levels of economic productivity through diversification, technological upgrading and innovation…” The article’s focus on industrial robots as a “significant technological breakthrough” that improves “labor productivity” and “total factor productivity” directly aligns with this target.
   • Target 8.4: “Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation…” The study’s central theme is examining whether the application of industrial robots (driving productivity and growth) can lead to a reduction in carbon emissions (environmental degradation), which is the essence of decoupling.

3. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.4: “By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes…” This target is central to the paper. The study analyzes the adoption of industrial robots (a clean technology) to make the manufacturing industry sustainable by reducing carbon emissions and improving efficiency. The heterogeneity analysis comparing developed and developing countries also reflects the clause “with all countries taking action in accordance with their respective capabilities.”
   • Target 9.b: “Support domestic technology development, research and innovation in developing countries…” The policy implications section discusses the need for developing countries to “enhance the absorptive capacity” for robot technologies and facilitate the “transfer of green technologies,” which supports this target.

4. SDG 12: Responsible Consumption and Production

   • Target 12.2: “By 2030, achieve the sustainable management and efficient use of natural resources.” The article explains that robots can “optimize the allocation of labor factors” and “improve the efficiency of production factor allocation,” which contributes to the efficient use of resources.

5. SDG 13: Climate Action

   • Target 13.2: “Integrate climate change measures into national policies, strategies and planning.” The article’s conclusion explicitly provides “policy implications,” advising that “countries should incorporate the application of industrial robots into the core of their national climate strategies,” directly addressing this target.

6. SDG 17: Partnerships for the Goals

   • Target 17.7: “Promote the development, transfer, dissemination and diffusion of environmentally sound technologies to developing countries…” The paper’s analysis of “technology spillovers” through the GVC and its policy recommendation to “facilitate the transfer of green technologies and capacity-building initiatives in developing countries” directly relates to this target.
   • Target 17.11: “Significantly increase the exports of developing countries…” The article investigates how robot adoption allows industries to climb the GVC, which enhances their “bargaining power and trade added value,” contributing to higher-value exports.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article uses several quantitative measures in its empirical analysis that serve as direct or proxy indicators for measuring progress towards the identified SDG targets.

• Indicators for SDG 7 & 9 (Energy Efficiency & Clean Technology)

  • Energy Intensity of the Industry (lnei): The article uses this as a control variable, defined as “the logarithm of the annual energy consumption divided by the total output of the industry.” This is a direct proxy for Indicator 7.3.1 (Energy intensity measured in terms of primary energy and GDP).
  • CO2 Emission per Unit of Value Added: One of the dependent variables used for robustness checks is “the logarithm of the ratio of carbon dioxide emissions to the value added by the industry (lnCI2).” This directly corresponds to Indicator 9.4.1 (CO2 emission per unit of value added).

• Indicators for SDG 8 (Economic Growth & Productivity)

  • Industrial Production Efficiency (pva): This is used as a control variable and is “measured by the value added per capita in the industry.” This serves as a proxy for Indicator 8.2.1 (Annual growth rate of real GDP per employed person).
  • Labor Substitution (Labor): The article constructs this mediating variable as “the expenditure on labor per unit of output.” It measures the efficiency of labor use and the impact of automation, which is relevant to tracking productivity changes.

• Indicators for SDG 13 (Climate Action)

  • Total Carbon Dioxide Emissions (ln CO2ijt): The primary dependent variable of the study is the “logarithmic transformation of the total carbon dioxide emissions.” This is a direct measure related to Indicator 13.2.2 (Total greenhouse gas emissions per year).

• Indicators for SDG 17 (Partnerships)

  • Global Value Chain Positioning Index (GVCPs): This mediating variable is used “to measure the positioning of various industries from different countries within the global value chain.” It reflects a country’s integration and status in global trade networks, which is an implicit measure of its participation in global partnerships and its export sophistication, relevant to Indicator 17.11.1.
  • Trade Openness (openness) and Foreign Direct Investment (FDI): Used as control variables, these are standard measures for a country’s integration into the global economy and are relevant for tracking global partnerships.

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators Identified in the Article
SDG 7: Affordable and Clean Energy	7.3: Double the global rate of improvement in energy efficiency.	Energy Intensity of the Industry (lnei): Log of energy consumption per total industry output.
SDG 8: Decent Work and Economic Growth	8.2: Achieve higher levels of economic productivity through technological upgrading and innovation. 8.4: Decouple economic growth from environmental degradation.	Industrial Production Efficiency (pva): Value added per capita in the industry. Labor Substitution (Labor): Labor expenditure per unit of output.
SDG 9: Industry, Innovation, and Infrastructure	9.4: Upgrade industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean technologies. 9.b: Support domestic technology development and innovation in developing countries.	Industrial Robot Usage Indicator (Robot): Stock of industrial robots per million hours of labor. CO2 emissions per value added (lnCI2).
SDG 12: Responsible Consumption and Production	12.2: Achieve the sustainable management and efficient use of natural resources.	Implied through analysis of optimizing resource allocation and improving production factor efficiency.
SDG 13: Climate Action	13.2: Integrate climate change measures into national policies, strategies and planning.	Total Carbon Dioxide Emissions (ln CO2ijt): The main dependent variable measuring industrial emissions.
SDG 17: Partnerships for the Goals	17.7: Promote the transfer and diffusion of environmentally sound technologies to developing countries. 17.11: Significantly increase the exports of developing countries.	Global Value Chain Positioning Index (GVCPs). Trade Openness (openness). Foreign Direct Investment (FDI).

Source: nature.com