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Article

Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk

by
Yuhan Xia
1,* and
Mahmood Ahmad
2,3
1
School of Marxism, Xidian University, Xi’an 710000, China
2
Advanced Research Centre, European University of Lefke, Lefke, Northern Cyprus, TR-10, Mersin 99010, Turkey
3
Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(5), 1960; https://doi.org/10.3390/su17051960
Submission received: 4 January 2025 / Revised: 12 February 2025 / Accepted: 19 February 2025 / Published: 25 February 2025
(This article belongs to the Special Issue Energy and Environment: Policy, Economics and Modeling)
Browse Figures
Figure 1
<p>ANN scheme. Source: authors constructed it using the MATLAB (version R2023B).</p> "> Figure 2
<p>ANN plots. Source: authors constructed it using the MATLAB (version R2023B).</p> "> Figure 3
<p>Error histogram. Source: authors constructed it using the MATLAB (version R2023B).</p> ">
Versions Notes

Abstract

:
In recent years, the integration of industrial robotics has emerged as a powerful tool in reshaping industries by enhancing production efficiency, reducing waste generation, and optimizing resource utilization. However, industrial robotics, particularly in manufacturing and production, require significant energy that can potentially impact on environmental quality. Despite the growing adoption of artificial intelligence (AI)-based industrial robotics, there is a paucity of literature on the impact of industrial robotics on the ecological footprint (EF), particularly in the context of advanced economies. In this context, this study aims to investigate the impact of industrial robotics, resource efficiency, energy transition, and geopolitical risk EF in G7 countries from 1993 to 2021. The study employed advanced econometric techniques, including Kernel-based Regularized Least Squares (KRLS) and Artificial Neural Network (ANN) machine learning methods. The results unveiled that industrial robotics significantly curtail environmental degradation by impeding the EF. Resource efficiency and energy transition posed a significant and negative impact on the EF. Geopolitical risks and economic growth exacerbate the EF. Based on the results, the study proposes important policy implications for achieving sustainable development.

1. Introduction

Climate change is one of the most pressing global challenges, with far-reaching consequences for human well-being and environmental and economic stability. This phenomenon can be attributed to unsustainable human activities, particularly linked with deforestation, industrialization, and the usage of fossil fuels. According to GFN [1], humanity is currently depleting nature 1.7 times faster than the pace at which the ecosystems of our world can replenish themselves. As a result, to address the increasingly pressing climatic and environmental concerns, both developed and developing nations worldwide have implemented various policies, including expediting energy transitions, preserving natural resources, and curbing industrial emissions to achieve sustainable development.
The advent of AI has had multifaceted impacts on industries and human life, with its applications expanding into healthcare, finance, logistics, energy, and manufacturing, among others. According to Forbes [2], the AI market is anticipated to accomplish an unprecedented USD 407 billion by 2027, a significant growth from its USD 86.9 billion revenue in 2022. AI, particularly through industrial robotics, can pose a favorable and unfavorable impact on the environment. On the negative side, industrial robotics require more energy to maintain and operate, which can lead to higher emissions, especially if the power is sourced from fossil fuels. The fast-evolving nature of technological change in robotics and AI may eventually lead to frequent replacement and upgradation, consequently resulting in electronic waste. On the other hand, AI-based automation can help the manufacturing industry at large in optimizing resources, minimalizing waste generation, and enhancing overall efficiency. The application of industrial robots encourages technological development in manufacturing, which is a significant path toward achieving energy efficiency and emission reduction [3].
Resource efficiency can help to alleviate the EF, considering how growth in the global population and consumption pattern goes beyond the unprecedented pressure faced by the Earth through its finite resources and fragile ecosystems. Kirikkaleli and Ali [4] suggest that resource efficiency is one of the viable approaches that can decouple economic development from environmental degradation by considering material, energy, and water use within cycles of production and consumption. It also entails several strategies, including circular economy principles, transition to renewable energy, the reduction of waste, and environmentally friendly technologies that enable more sustainable means of production. In light of the urgent need for a shift toward sustainability, resource efficiency has emerged as one of the most critical factors that can help balance human development with environmental stewardship.
Energy is considered an indispensable pillar of economic and human development. Fossil fuel energy consumption is the prime source of environmental deprivation, responsible for approximately 75% of global greenhouse gas emissions. Nonetheless, nations rely on fossil fuels such as coal, oil, and gas to meet their energy needs, further exacerbating environmental degradation. Therefore, achieving SDG-13 necessitates the simultaneous fulfillment of SDG-7, which is access to clean and affordable energy. Addressing the current climate crisis requires transitioning from conventional energy sources to renewable and clean alternatives. This shift is widely recognized as a critical component of effective climate change mitigation strategies. The International Energy Agency projected that to keep global warming to 1.5 °C, the world will need triple the amount of renewable energy capacity by 2030—that is, at least 11,000 GW—and double the average annual rate of improvement in energy efficiency worldwide from about 2% to over 4% until 2030 [5]. Renewable energy transition requires significant financial resources along with consistent government policies.
Geopolitical risk plays a critical role in shaping global economic and environmental outcomes. Geopolitical risk can influence the EF in several ways. First, political instability and disputes between countries can disrupt energy markets. Supply chain disruption and price volatility may, therefore, compel countries to opt for fossil fuel energy at the expense of long-term green transition in energy. Second, geopolitical conflict diverts funds and investment meant for projects on the mitigation of climate change to defense and security sectors. Third, geopolitical issues can disrupt the supply chain for renewable energy technologies, thereby hindering the nation’s efforts toward energy transition. According to Pata et al. [6], geopolitical risk is often perceived as unfavorable. However, it can inadvertently improve environmental quality since it may disrupt economic activities or energy consumption, lowering emissions. Furthermore, as countries face economic sanctions related to the import of fossil fuels, it may accelerate the transition to green energy sources [7].
The G7 is an economic bloc comprising seven of the world’s advanced economies, which have contributed much to the world, particularly in aspects of economic development, trade, global governance frameworks, technological advancement, and leading on the fronts of climate change mitigation and low-carbon transitioning. They collectively contributed 25.8% of the global GDP in 2023. In 2023, these countries consumed 158.6 exajoules of primary energy consumption, which is 25.6% of the global energy consumption of 620 exajoules. These countries jointly emitted 7626.2 million tons of carbon dioxide, which accounted for 21.71% of the global total emissions. The G7 countries are committed to reducing emissions by 19–33% by 2030 compared to 2019 levels and achieving carbon neutrality (‘net zero’) by 2050. As highlighted at the IEA 2024 Ministerial, the G7 must also emphasize that energy security and climate security are inherently interconnected and stress that clean energy transitions are essential for ensuring energy security. The COP28 agreement to double energy efficiency rates and triple renewable energy capacity by the end of this decade has the potential to achieve 85% of the reductions in unabated fossil fuel use needed by 2030. However, despite these efforts, their EF in 2022 reached 5.04 gha per person, which is considerably higher than the world average of 2.58 gha per person. Furthermore, climate analysis reports reveal that none of the G7 members are currently on track to meet their existing 2030 emission reduction targets, which remain misaligned with the 1.5 °C pathway [8]. In summary, these arguments underscore the necessity for redesigning their climate mitigation policy framework, particularly in light of challenges posed by geopolitical risks.
The objective of this study is to examine the impact of industrial robotics, resource efficiency, energy transition, and geopolitical risk on environmental sustainability in G7 countries. This study addresses the following research questions: (i) What is the impact of industrial robotics on environmental quality in G7 countries? (ii) Does an increase in resource efficiency enhance environmental quality? (iii) Does accelerating the energy transition process help mitigate environmental degradation? (iv) What is the impact of geopolitical risk on environmental sustainability? This study makes three key contributions to the literature on environmental sustainability. First, it investigates the impact of industrial robotics on environmental quality in G7 countries, addressing a critical gap in the literature. While AI-based robotics are increasingly recognized for their transformative potential, there is limited empirical research on how AI-driven automation in manufacturing influences ecological outcomes, particularly in advanced economies. By focusing on G7 nations—global leaders in technology and environmental policy—this study provides novel insights into how AI-based industrial robotics can shape environmental sustainability. Second, the study uniquely integrates industrial robotics, resource efficiency, energy transition, and geopolitical risk within a single environmental policy framework. This holistic approach provides a more comprehensive understanding of the complex interplay between technological advancement, resource management, energy policies, and global political dynamics in shaping environmental outcomes. Third, the study employed advanced econometric techniques, including Kernel-based Regularized Least Squares (KRLS) and Artificial Neural Network (ANN) machine learning methods, allowing for a more nuanced and robust analysis. Lastly, by focusing on G7 countries, our research offers targeted policy recommendations for advanced economies striving to meet ambitious environmental targets, providing valuable insights for global leadership in tackling climate change and advancing SDGs.
The remainder of the study is structured as follows: Section 2 presents the literature review, followed by the materials and methods in Section 3. Section 4 outlines the results and discussion, while Section 5 concludes with policy implications.

2. Literature Review

2.1. Industrial Robotics and Environmental Quality

Industrial robotics powered by AI, has vastly proliferated in several industries to increase output precision, bring about high efficiency, and assure safety at work, leading to more sustainable practices. For instance, in the field of manufacturing, robotics can optimize resource usage, reduce energy consumed, and lower waste levels by carrying out high-level tasks with accuracy and consistency. However, the large-scale usage of industrial robotics creates new challenges in the form of energy consumption and possible long-term electronic waste. In this perspective, Chen et al. [9] explored the impact of the usage of industrial robotics on the EF in 76 countries and found that industrial robotics mitigate the EF. The authors further highlighted that industrial robotics decrease the EF through energy upgrading, green employment, and time-saving effects. However, it increases the EF through the industrial driving effect. J. Wang et al. [10] advocated that although industrial robots curtail environmental pollution, they may also contribute to an energy rebound effect, which will offset its impact on emissions. Likewise, L. Liu et al. [11] studied the impact of industrial robots on the EF in the top 10 leading industrial artificial intelligence nations from 2007 to 2022. Using the quantile-on-quantile method, they found that industrial robotics mitigates environmental deterioration.
On the flipside, Luan et al. [12] revealed that the use of industrial robotics exacerbates air pollution and greenhouse gas emissions because it expands consumption and production, which requires more energy, leading to higher emissions. B. Liu et al. [13] argue that industrial robotics initially increase environmental degradation but decrease it after reaching a certain level, confirming an inverted U-shaped association. They highlighted that the effect of industrial robotics on the environment depends on the country’s environmental regulations, digital endowment, and skilled labor force. Yang and Liu [14] explored the effect of industrial robotics on green growth and found that industrial robotics has no significant effect on the green growth of China. The study formulates the following hypothesis:
H1: 
Industrial robotics exert a significant negative impact on the ecological footprint.

2.2. Resource Efficiency and Environmental Quality

Resource efficiency and the quality of the environment are interrelated concepts that have gained significant attention over the past years following sustainability and environmental degradation concerns. Previous studies highlighted resource efficiency as a way of improving environmental quality. For instance, Kirikkaleli [15] explored the influence of resource efficiency and energy productivity on CO2 emissions and uncovered that improved resource efficiency significantly curtails environmental deprivation in Japan. The author suggested policymakers lessen the use of fossil energies and promote the sustainable utilization of natural resources to ensure sustainable growth. Likewise, in the context of Finland, Alola and Adebayo [16] gauged the impact of resource efficiency and green technologies on greenhouse gas emissions. Their results highlighted that a rise in raw material production improves environmental quality by mitigating greenhouse gas emissions. Using quantile regression, Zhang et al. [17] found that resource efficiency promotes sustainable development in OECD nations. The authors suggested policymakers focus on resource management to promote economic growth and improve environmental quality. Kirikkaleli and Ali [4] studied the impact of resource efficiency on carbon emissions in Germany. Using the NARDL method, the authors unveiled that a positive shock in resource efficacy reduces emission levels, while a negative shock exacerbates environmental degradation. Hence, we develop the following hypothesis:
H2: 
Higher resource efficiency leads to a decreased ecological footprint.

2.3. Energy Transition and Environmental Quality

Shifting from fossil fuels to renewable energy sources is essential for mitigating climate change and achieving environmental sustainability objectives. This transition ensures more sustainable resource use and minimizes biodiversity loss. Murshed et al. [18] studied the impact of ET on carbon emissions in South Asian countries and found that ET significantly curtailed emission levels in the short and long run. The author further highlighted that trade integration among countries also escalates this effect. However, Onifade and Alola [19] argue that although eco-innovation helps E-7 countries upsurge the share of green energy sources and reduce the negative effect of undesirable impact from primary energy, the impact is not enough to trigger ET, which could reduce environmental degradation. Recently, Bulut et al. [20] determined that renewable energy initially negatively impacted the environment, but after reaching a certain level, it started to improve environmental quality in Turkiye. In the context of OECD countries, Afshan et al. [21] concluded that ER significantly impedes the EF. Additionally, the author found one-way causality from ET to the EF, which indicates that changes in ET can affect the EF but not the other way around. Dao et al. [22] argue that ET acts as two double-edged swords in affecting the EF. They found that ET improves ecological quality in countries with bad environmental conditions while harming environmental quality in nations that already have better ecological conditions. In the context of the top ten emitting nations, Wang et al. [23] also found that ET significantly curtails the emission level. Cao et al. [24] suggested boosting ET and adopting a circular economy model to decrease the EF for a sustainable future. These arguments motivated us to propose the following hypothesis:
H3: 
An accelerated energy transition leads to a reduced ecological footprint.

2.4. Geopolitical Risk and Environmental Quality

In the last two decades, geopolitical risk has emerged as one of the significant determinants influencing environmental quality. Geopolitical risk encapsulates all uncertainties stemming from political instability, international conflict, trade disruption, and policy uncertainty that can influence countries’ efforts toward environmental sustainability. According to W. Wang et al. [25], GPR helps lower CO2 emissions. The authors argue that GPR is a key factor in determining investment decisions, which can impact economic activities in the country. The change in investment decisions due to GPR might be the reason for the negative impact of GPR on CO2 emissions. However, on the contrary, Chu et al. [26] unveiled that GPR escalates environmental degradation in middle- and high-income countries. They further argue that GPR influences consumer and corporate behavior, which leads to changes in the consumption and production patterns of a country. Furthermore, GPR results in energy instability. For instance, companies with substantial financial resources tend to rely on traditional energy sources and postpone the transition to less energy-intensive and more environmentally friendly technologies. Villanthenkodath and Pal [7] found that GPR improves environmental quality by mitigating CO2 emissions and the EF but deteriorates by negatively affecting the load capacity factor. Li et al. [27] unveiled that an increase in GPR escalates environmental degradation, while GPR moderates the association between economic growth and CO2 emissions.
H4: 
Higher geopolitical risk is positively associated with the ecological footprint.

2.5. Literature Gap

Summing up the literature review, it can be inferred that limited studies investigated the impact of industrial robotics on environmental quality. Notably, there is a lack of comprehensive studies specifically focusing on G7 countries as a cohesive group despite their economic and technological significance. The existing literature often examines these factors in isolation, neglecting the potential impact of artificial intelligence, resource efficiency, energy transition, and geopolitical risk on environmental quality in the same environmental policy framework. The absence of a comprehensive framework integrating these factors with the SGGs for G7 countries is particularly noticeable. Moreover, there is a dearth of research employing advanced machine-learning techniques to analyze these multifaceted relationships. Addressing these gaps would provide crucial insights for G7 policymakers, enabling more effective strategies for achieving environmental sustainability while navigating the challenges and opportunities presented by technological advancements, resource management, energy transition, and geopolitical risks.

3. Materials and Methods

3.1. Empirical Model and Data

The study followed the research of Yang and Liu [14] and Wang et al. [28] to construct the following model.
EF i t = τ 0 + ϑ 1 IR i t + ϑ 2 RE i t + ϑ 3 ET i t + ϑ 4 GPR i t + ϑ 5 GDP i t + η i t
In Equation (1), EF represents the dependent variable, while. IR, RE, ET, and GPR are independent variables, denoting industrial robotics, resource efficiency, energy transition, and geopolitical risk, respectively. GDP serves as a control variable representing economic growth. All variables are converted to natural logarithm form to standardize the data. The subscript i indicates the countries (in this case, the G7 nations), while t denotes the time dimension. The ϑ symbols represent the coefficients while τ and η represent the constant and error term, respectively.
The study is conducted using the yearly data from 1993 to 2021 for G7 countries (United States, Germany, Japan, France, Canada, Italy, and the United Kingdom). The starting year was 1993, based on the availability of industrial robotics data, while the year ended in 2021 due to the availability of ET data. The EF is computed in Gha per capita footprint, which is cumulative of six factors: carbon, forest products, fishing ground, cropland, built-up land, and grazing land. The data on EF were obtained from the GFN (2023). IR is proxied by robot usage stock per 1000 workers, and the data were obtained from the IFR [29]. Resource efficiency is calculated by GDP per person divided by material footprint per capita (tonnes). The data on material footprint were obtained from Hickel [30]. Energy transition (measured by renewable energy use as a percentage of final energy) data were sourced from Caldara and Iacoviello [31]. The GPR index is based on newspaper articles covering geopolitical tensions, and data were acquired from Caldara and Iacoviello [31]. Economic growth is measured by GDP per capita (constant USD 2015), and data were accessed from WDI [32]. All variables were converted to natural logarithm form to standardize the data. Table 1 provides a description of the variables.

3.2. Estimation Strategy

In terms of estimation strategy, the study initially used the Pesaran [34] test to gauge the presence of CSD in the dataset. If the CSD is disregarded, parameter estimations may become misleading, resulting in incorrect statistical conclusions. The results of this test can assist in selecting the most appropriate estimating strategies for dealing with CSD, boosting the reliability of the empirical analysis. In the following stage, the slope heterogeneity test provided by Pesaran and Yamagata [35] was used to determine if slope parameter heterogeneity exists in the panel dataset. Despite numerous similarities, countries differ in geography, economic development, culture, and policies. Ignoring slope heterogeneity may invalidate the findings and result in inaccurate policy recommendations. The stationary characteristics of each variable were examined using the two approaches given by Pesaran [36], namely CIPS and CADF. Many econometric analyses rely heavily on stationarity analysis, as non-stationary data can result in misleading regressions and incorrect results. These approaches outperform the first-generation methods in terms of robustness for small sample numbers and CSD.
In order to test whether there is a cointegration association in the model, the study utilized ECM-based Westerlund’s [37] panel cointegration test. The benefit of this approach is that it is able to counter some of the constraints faced by the previously commonly used residual basis cointegration tests (e.g., Pedroni, Kao, etc.). Specifically, this ECM-based test considers possible CSD among variables and provides four different test statistics to provide robust results.
Once the cointegration is confirmed, the study utilized FMOLS and DOLS estimation methods to gauge the long-run impact of explanatory variables on EF. These estimation methods effectively address serial correlation and endogeneity problems, which are common in the long-run relationship. FMOLS excels in handling CSD and non-stationary data, while DOLS offers flexibility through its lag structure and provides efficient results in a small sample size.
For the robustness test, the study employed the KRLS method proposed by Hainmueller and Hazlett [38]. The KRLS is a machine-learning-based approach that offers reliable findings compared to traditional estimation methods. According to Hainmueller and Hazlett [38], KRLS is an advanced technique for addressing regression and classification issues that do not rely on linearity or additivity assumptions by taking advantage of machine learning. Additionally, the study employed the ANN method. ANN consists of input, hidden, and output layers, each composed of processing units called neurons. These neurons, the fundamental components of ANN operations, calibrate specific mathematical functions to process inputs and generate outputs. Information flows through the network as neurons transmit processed data to subsequent layers. Interconnections between neurons are quantified by numerical weights assigned to the links between layers. Unlike statistical models, ANN can autonomously adjust these weights to enhance performance. Though ANN and conventional statistical approaches differ methodologically, both contribute to a robust analytical framework. While ANNs are commonly employed across various research fields, their application in environmental economics remains limited. The study includes a diagrammatic representation of the ANN structure in Figure 1.

4. Results and Discussion

4.1. Results

The descriptive statistics in Table 2 indicate the notable variations across the variables. The average EF is 6.248, with a minimum of 3.461 and a maximum of 10.926. IR reveals substantial variability with a mean value of 106.9 and a maximum value of 412.961. Resource efficiency has the second highest standard deviation value of 396.402, with a minimum of 923.686 and a maximum of 2710.55. The mean value of ET is 9.834, while GPR has a mean value of 0.662. The GDP per capita averages USD 38,410.85, ranging from USD 27,851.10 to USD 61,829.80.
The VIF test results in Table 3 indicate that all variable values are less than 5, with GPR having the highest at 3.28, followed by the GDP at 2.81. The average VIF value is 2.02, suggesting that the model is free from multicollinearity issues.
The Pesaran [34] test results in Table 4 indicate the presence of CSD across all the variables. All the CD statistics are positive and highly significant at the 1% level, with all variables having a p-value of 0.000. Among them, the largest value of the CD statistic is 20.69 for the EF, followed by 20.043 for the GDP. The absolute correlation values range from 0.681 to 0.838, indicating high cross-sectional correlations. This implies that a shock affecting one cross-sectional unit is likely to spill over into another, highlighting the interdependence of the G7 countries. The presence of CSD requires appropriate methods to handle this issue and provide efficient results.
The slope heterogeneity test results in Table 5 reject the null hypothesis of slope homogeneity at a 1% significance level. These findings indicate that despite many similarities, the G7 may vary in terms of innovation, GPR, and the economic growth level. Ignoring slope heterogeneity may lead to biased results.
Table 6 presents the results of the panel CADF and CIPS unit root tests. The findings indicate that IR, RE, ET, and the GDP are integrated at the level, while the EF and GPR exhibit unit roots. However, upon taking the first difference, all variables become stationary.
The findings of the panel cointegration test are presented in Table 7. The results show that Gt, Pt, Pt, and Pa test statistics are statistically significant, which is evident from the robust probability values. The results support the long-term equilibrium association between the variables.
The results from the FMOLS estimation, presented in Table 8, indicate that the coefficient of industrial robotics (IR) is negative and statistically significant. Specifically, a 1% increase in IR stocks reduces the ecological footprint (EF) by 0.049%. Similarly, the coefficient of resource efficiency (RE) is significantly negative, suggesting that a 1% improvement in resource efficiency reduces the EF by 0.773%. Energy transition (ET) also exhibits a negative and significant coefficient of −0.045 at the 1% level, implying that a 1% increase in ET decreases the EF by 0.045%. In contrast, geopolitical risk (GPR) has a positive and significant coefficient of 0.021 at the 5% level, indicating that a 1% rise in geopolitical risk leads to a 0.021% increase in EF. Additionally, the GDP shows a positive and highly significant coefficient of 0.510 at the 1% level, suggesting that a 1% increase in the GDP corresponds to a 0.510% rise in the EF. The results from the DOLS estimation further support these findings, reinforcing the robustness of the FMOLS results.
For the robustness test, the research used an alternative technique of KRLS, and the results are given in Table 9. The findings denote that IR, RE, and ET coefficients are negatively related to EF, while GPR and the GDP positively impact the EF. Thus, these findings strongly aligned with the results of FMOLS and DOLS. Therefore, the study’s findings are robust and do not differ from the estimation methods.
Finally, the ANN model is employed to predict the target variables based on the input features, and its performance is evaluated to assess the accuracy of these predictions. The study used five input variables (IR, RE, ET, GPR, and GDP) and one target variable (EF). Additionally, 70% of the data was used for training, 15% for validation, and 15% for testing, with the layer size set to 10. Figure 2 illustrates the model’s performance across different datasets: Training, Validation, Test, and All (combined). The high R-values (ranging from 0.97948 to 0.99234) indicate strong correlations between the predicted and actual values. Furthermore, the error histogram in Figure 3 shows that the value of 0.0036 is very low, demonstrating the excellent predictive performance of the ANN model.

4.2. Discussion

The results demonstrate that industrial robotics exert a significant negative impact on the EF. The negative effect portrays that the increased adoption of industrial robotics can help to curtail the EF. These results align with the notion that the automation of industries can optimize resource utilization, reduce waste, and increase efficiency in manufacturing. For instance, robots operate with greater precision, producing less material waste and energy consumption. They also work in conditions unsuitable for humans, saving factories energy for climate control. Robotic waste management and recycling systems can improve sorting efficiency, contributing to the circular economy. These findings are particularly pertinent for G7 countries, which are at the forefront of adopting advanced technologies and possess well-developed manufacturing sectors. The integration of industrial robotics aligns with the environmental agendas of these nations, enabling them to meet their commitments to reducing emissions and promoting sustainability. The results are consistent with the studies by Chen et al. [9] and L. Liu et al. [11] while contradicting those of Luan et al. [12].
The negative impact of resource efficiency on the EF in G7 countries underlines the critical contribution of optimized resource use in advanced economies. These results align with the G7 countries’ commitments to sustainable development and circular economy. These countries are leading the world in terms of technological advancement and economic output. As global leaders in technological innovation and economic output, G7 countries are uniquely positioned to demonstrate how enhanced resource efficiency can effectively decouple economic growth from environmental harm. This decoupling is essential for achieving long-term sustainability and reducing the ecological footprint of industrial activities. The observed results can be attributed to several key factors prevalent in G7 countries. These include the adoption of advanced waste management systems, the implementation of energy-efficient technologies, and the integration of sustainable manufacturing processes. Additionally, stringent environmental regulations and heightened consumer awareness in these nations have likely incentivized businesses to prioritize resource efficiency and adopt environmentally responsible practices. Moreover, the findings underscore the importance of resource efficiency as a strategic tool for achieving the United Nations SDGs. This is particularly relevant in the context of global efforts to combat climate change, where resource efficiency can play a pivotal role in reducing greenhouse gas emissions and conserving natural resources. This finding aligns with the results of Kirikkaleli [15] and Zhang et al. [17].
The negative effect of ET on the EF indicates that the energy transition can effectively curb the EF across G7 nations. These findings are justifiable because recently, G7 countries have been making efforts to decrease their dependence on fossil fuels and foster the share of renewables. The adoption of wind, solar, and hydropower energy not only reduces greenhouse gas emissions but also contributes to biodiversity conservation and ecosystem preservation by minimizing the environmental damage associated with fossil fuel extraction and consumption. G7 countries have implemented strict environmental regulations to limit the use of fossil fuels, coupled with favorable policy frameworks that incentivize renewable energy adoption. For instance, subsidies for renewable energy projects, carbon pricing mechanisms, and investments in green infrastructure have accelerated the energy transition in these nations. Advancements in renewable energy technologies have made wind, solar, and hydropower more cost-effective and scalable, further driving their adoption. These findings are similar to Dao et al. [22] and Cao et al. [24].
The significant positive impact of geopolitical risk (GPR) on the EF highlights the detrimental effects of political instability and international tensions on environmental sustainability in G7 countries. Geopolitical conflicts and uncertainties often shift national priorities away from environmental protection, as resources and attention are diverted to addressing immediate security and economic concerns. For instance, geopolitical instability can exacerbate energy security concerns, prompting countries to rely on polluting energy sources, such as coal and oil, rather than advancing the adoption of renewable energy [39]. Moreover, geopolitical risks can disrupt international supply chains for renewable energy technologies, hindering the energy transition. Trade disputes, sanctions, and diplomatic tensions may delay or prevent the import of critical components needed for renewable energy infrastructure, such as solar panels or wind turbines. Additionally, geopolitical uncertainties create an unfavorable environment for green investments, as investors may perceive renewable energy projects as riskier during periods of instability. This reluctance to invest in greener technologies further slows progress toward sustainability [40]. These results are corroborated by Li et al. [27] and Chu et al. [26].
The unfavorable impact of the GDP on the environment in G7 countries indicates the continuing challenges in balancing economic development with ecological preservation within advanced economies. Despite their technological advancement and commitment to carbon neutrality, these countries struggle to align their economic development path with the SDGs. This relationship can be attributed to the increased production and consumption patterns that accompany economic growth, which drive higher levels of resource extraction, energy consumption, and waste generation. As economies expand, the demand for goods and services rises, often leading to greater environmental degradation unless mitigated by sustainable practices. This finding highlights the need for G7 countries to restructure their economic models to decouple growth from environmental harm. Emphasizing circular economy principles, such as reducing waste, reusing materials, and recycling resources, can help minimize the ecological footprint of economic activities. These results are consistent with the Ahmed et al. [41] and Cosimo [42].

5. Conclusions

This study explored the impact of industrial robotics, resource efficiency, ET, and the GPR on the EF of G7 nations from 1993 to 2021. The empirical outcomes reveal that industrial robotics significantly and negatively affects the EF, indicating that increased adoption of industrial robotics can help reduce environmental degradation. Moreover, resource efficiency also negatively impacts the EF, highlighting the importance of optimized resource use in advanced economies. Energy transition demonstrates a negative effect on the EF, suggesting that the shift to renewable energy sources effectively curbs environmental impact. Conversely, the GPR poses a significant positive impact on the EF. The positive association between the GPR and EF suggests that geopolitical instability can obstruct environmental progress. Heightened geopolitical tensions may divert policy priorities away from sustainability initiatives, delay green investments, and increase reliance on non-renewable energy sources due to supply chain disruptions. Economic growth shows a positive relationship with the EF, underscoring ongoing challenges in balancing development with ecological preservation.
Based on the study’s findings, several policy implications emerge to guide G7 countries in achieving environmental sustainability and advancing the SDGs: First, governments should incentivize the adoption of AI-based industrial robotics in manufacturing and other sectors by offering tax benefits, subsidies, or grants for companies investing in green automation technologies. Funding research and development in AI applications that enhance resource efficiency, reduce waste, and minimize environmental impact can further accelerate sustainable industrial practices. These measures align with SDG 9 and SDG 12. Second, policies should encourage circular economy principles, such as designing products for durability, repairability, and recyclability. Implementing extended producer responsibility (EPR) policies can ensure that manufacturers optimize resource use throughout product lifecycles, reducing waste and promoting sustainable consumption. These efforts support SDG 12 and SDG 13. Third, to achieve SDG 7, governments should increase funding for green energy infrastructure and research. Implementing carbon pricing mechanisms and phasing out fossil fuel subsidies can accelerate the transition to renewable energy sources. Additionally, investing in smart grid technologies can improve energy distribution efficiency and integrate renewable energy into national grids more effectively. Fourth, to address the adverse effects of geopolitical risks on environmental sustainability, G7 countries should foster international cooperation on climate and energy issues. Developing resilient supply chains for critical green technologies, such as solar panels and wind turbines, can minimize disruptions during geopolitical crises. These measures align with SDG 16 and SDG 17. Lastly, G7 countries should prioritize qualitative growth over quantitative expansion by promoting environmentally friendly manufacturing processes and sustainable business practices. Targeted policies and incentives can encourage industries to adopt low-carbon technologies and green innovations, contributing to SDG 8 and SDG 9. By implementing these policies, G7 countries can leverage their technological and economic advantages to lead global efforts in combating climate change, achieving environmental sustainability, and attaining the SDGs.
This study is limited to G7 countries, meaning its findings may not be directly applicable to other nations with different economic conditions and policy environments. Additionally, the analysis focuses specifically on industrial robotics, which is just one aspect of artificial intelligence (AI). Future research could expand the scope by incorporating other AI technologies, such as machine learning, deep learning, and automation, to provide a more comprehensive understanding of AI’s influence on green growth. Moreover, examining a broader range of countries, including developing and emerging economies, could offer deeper insights into the global implications of AI for sustainable development.

Author Contributions

Y.X.: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing—Original Draft. M.A.: Writing, review and editing, validation. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the National Social Science Fund Youth Project 22CKS037.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset used in the study is publicly available, except for the robotics data, and the authors mention the data sources in Table 1. It is also accessible from the corresponding author upon request.

Acknowledgments

During the preparation of this work the author(s) used ChatGPT in order to improve language and readability. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
Nomenclature
AIArtificial Intelligence
ANNArtificial Neural Network
CADFCross-sectionally Augmented Dickey-Fuller
CIPSCross-sectionally augmented Im, Pesaran, and Shin
CSDCross-Sectional Dependency
DOLSDynamic Ordinary Least Squares
EFEcological Footprint
ETEnergy Transition
FMOLSFully Modified Ordinary Least Squares
GDPGross Domestic Product
GPRGeopolitical Risk
KRLSKernel-based Regularized Least Squares
IRIndustrial Robotics
REResource Efficiency
SDGsSustainable Development Goals
VIFVariance Inflation Factor

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Sustainability 17 01960 g001
Figure 1. ANN scheme. Source: authors constructed it using the MATLAB (version R2023B).
Figure 1. ANN scheme. Source: authors constructed it using the MATLAB (version R2023B).
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Figure 2. ANN plots. Source: authors constructed it using the MATLAB (version R2023B).
Figure 2. ANN plots. Source: authors constructed it using the MATLAB (version R2023B).
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Figure 3. Error histogram. Source: authors constructed it using the MATLAB (version R2023B).
Figure 3. Error histogram. Source: authors constructed it using the MATLAB (version R2023B).
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Table 1. Variable description.
Table 1. Variable description.
VariableAcronymMeasurement Source
Ecological footprintEFGha per capitaGFN [33]
Industrial roboticsIRRobot usage stock/1000IFR [29]
Resource efficiencyREGDP per capita/Material footprint per capita Author’s calculations
Energy transitionETRenewable energy use (% of final energy)WDI [32]
Geopolitical riskGPRIndex (higher values denote increased risk, while lower values signify decreased risk).WDI [31]
Economic growthGDPPer capita (constant USD 2015)WDI [32]
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariableMeanMinMaxStd. dev.25% Quantile50% Quantile75% Quantile
EF6.2483.46110.9261.8495.0085.6407.947
IR106.90412.961122.63615.59149.312167.579
RE1630.925923.6862710.55396.4021322.1501651.0701838.351
ET9.8340.8023.906.5424.5208.65014.200
GPR0.6620.0614.3490.7670.1720.3550.802
GDP38,410.85027,851.1061,829.87438.27332,612.20036,203.10742,639.552
Source: Authors’ own calculations.
Table 3. Multicollinearity–VIF test.
Table 3. Multicollinearity–VIF test.
VariableVIF1/VIF
GPR3.280.305
GDP2.810.556
ET1.570.636
RE1.270.788
IR1.150.867
Mean VIF2.02
Note: VIF > 5 indicates moderate, and VIF > 10 suggests severe multicollinearity. Source Authors’ own calculations.
Table 4. CSD test.
Table 4. CSD test.
VariableCDp-ValueAbs(corr)
EF20.69 *0.0000.838
IR9.762 *0.0000.708
RE16.367 *0.0000.681
ET19.613 *0.0000.795
GPR17.514 *0.0000.710
GDP20.043 *0.0000.812
Note: asterisk * represents 1% significance level. Source: Authors’ own calculations.
Table 5. Slope heterogeneity test results.
Table 5. Slope heterogeneity test results.
TestTest Stat.Prob.
Δ ˜ 9.664 *0.000
Δ ˜ a d j u s t e d 11.096 *0.000
Note. * depicts level of significance at 1%. Authors’ own calculations.
Table 6. Panel unit root test outcomes.
Table 6. Panel unit root test outcomes.
VariableCIPSCADF
I (0)I (1)I (0)I (1)
EF−2.700 *−5.516 *−2.447 **−4.083 *
IR−1.141−3.133 *−1.5913.651 *
RE−1.797−4.624 *−1.697−3.348 *
ET−1.782−5.642 *−1.540−4.213 *
GPR−3.041 *−5.856 *−2.609 **−4.162 *
GDP−1.118−3.859 *−1.386−3.078 *
Note. *, and ** depict 1%, and 5% significance level. cv10 = −2.21, cv5 = −2.33, and cv1= −2.57. Source: Authors’ own calculations.
Table 7. Panel cointegration test.
Table 7. Panel cointegration test.
StatisticValueZ-Valuep-ValueRobust p-Value
Gt−3.452 *−3.2610.0010.006
Ga−12.958 *−0.4250.3350.006
Pt−8.435 **−2.8290.0020.012
Pa−12.143 *−1.3680.0860.008
Note. *, and ** depict 1%, and 5% significance levels, respectively. Bootstrapping = 500. Source: Authors’ own calculations.
Table 8. Long-run estimation.
Table 8. Long-run estimation.
FMOLSDOLS
VariableCoefficientT-StatCoefficientT-Stat
IR−0.049 *−5.529−0.050−11.425
RE−0.773 *−15.724−0.578−19.283
ET−0.045 *−3.895−0.048−6.707
GPR0.021 **2.0580.016 ***1.973
GDP0.510 *7.1600.59829.796
R-squared0.959—0.987—
Adjusted R-squared0.957—0.968—
Long-run variance0.006—0.001—
Note. *, **, and *** depict 1%, 5%, and 10% significance level. Source: Authors’ own calculations.
Table 9. Machine learning regression (KRLS) results.
Table 9. Machine learning regression (KRLS) results.
VariableAvg.SET-StatProb.P25P50P75
IR−0.048 *0.003−15.0310.000−0.083−0.053−0.011
RE−0.566 *0.022−25.8770.000−0.843−0.555−0.429
ET−0.038 *0.007−5.8410.000−0.084−0.046−0.009
GPR0.051 *0.0067.8840.0000.0190.0520.084
GDP0.412 *0.03611.2880.0000.2260.4330.577
Note. * depict 1% significance level. Source: Authors’ own calculations.
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MDPI and ACS Style

Xia, Y.; Ahmad, M. Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk. Sustainability 2025, 17, 1960. https://doi.org/10.3390/su17051960

AMA Style

Xia Y, Ahmad M. Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk. Sustainability. 2025; 17(5):1960. https://doi.org/10.3390/su17051960

Chicago/Turabian Style

Xia, Yuhan, and Mahmood Ahmad. 2025. "Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk" Sustainability 17, no. 5: 1960. https://doi.org/10.3390/su17051960

APA Style

Xia, Y., & Ahmad, M. (2025). Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk. Sustainability, 17(5), 1960. https://doi.org/10.3390/su17051960

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