Barriers and Opportunities in Implementing Carbon Neutrality Goals in China’s Heavy Industries

Abstract

The transition to carbon neutrality in China’s heavy industries is essential for mitigating global climate change, given the sector’s significant contribution to greenhouse gas emissions. This study systematically examines the multifaceted barriers and opportunities influencing decarbonization efforts in these industries. Employing expert-driven methodologies, including the Delphi method and the best-worst method (BWM), the research identifies and prioritizes critical challenges, such as the high upfront costs for renewable technologies, the technological dependency on coal, and the fragmented regulatory frameworks. It highlights transformative opportunities, emphasizing advancements in carbon capture, utilization, and storage (CCUS), renewable energy integration, and strengthened carbon pricing mechanisms. The findings reveal the interaction between economic, technological, and policy dimensions, underscoring the need for coordinated interventions to overcome entrenched barriers. The study contributes to theoretical advancements by integrating expert insights with robust multi-criteria decision-making techniques and offers actionable pathways for policymakers and industry stakeholders to accelerate industrial decarbonization. These insights align with international climate objectives, providing a scalable framework for global sustainable transitions in energy-intensive sectors.

1. Introduction

The transition to carbon neutrality has emerged as a global imperative, driven by the escalating impacts of climate change and the urgent need for sustainable practices. Among the most significant contributors to greenhouse gas emissions are heavy industries, including steel, cement, and chemicals, which collectively account for approximately 20% of global emissions [1]. These industries are characterized by their high energy consumption and reliance on carbon-intensive processes, making them both critical to economic development and significant obstacles to achieving global climate targets. Addressing the emissions from these sectors is not merely a regional or national challenge but a global priority, as the success of decarbonization efforts in heavy industries will significantly influence the trajectory of global temperature rises and the achievement of the goals outlined in the Paris Agreement [2].

China stands at the forefront of this challenge, both as the world’s largest industrial producer and consumer and as the largest emitter of carbon dioxide. The country’s heavy industries are integral to its economic structure, contributing substantially to GDP and employment while also serving as critical nodes in global supply chains [3]. At the same time, these industries are heavily reliant on coal, the most carbon-intensive fossil fuel, which underpins a significant portion of their energy consumption and production processes. In recognition of its pivotal role in the global decarbonization landscape, China has committed to achieving carbon neutrality by 2060, a target that necessitates profound transformations across all industrial sectors [1]. The decarbonization of its heavy industries, therefore, represents not only a cornerstone of its national climate strategy but also a critical component of the global efforts to mitigate climate change.

Despite the urgency and importance of these efforts, the decarbonization of China’s heavy industries is fraught with challenges. Technological barriers remain a key impediment, as many industries rely on outdated systems that are incompatible with low-carbon technologies. Carbon capture, utilization, and storage (CCUS), a critical innovation for reducing emissions in hard-to-abate sectors, has seen limited deployment due to its high costs and technical constraints [4]. Similarly, the integration of renewable energy into industrial processes is hampered by infrastructure limitations and the intermittent nature of renewable energy sources. These technological challenges are compounded by policy and regulatory constraints. Inconsistent and fragmented policy frameworks have created uncertainty, deterring long-term investments in green technologies. The weak enforcement of emission standards further exacerbates this issue, as non-compliance undermines the efficacy of existing regulations. Moreover, a lack of coordination among sectors and across levels of governance has led to inefficiencies and missed opportunities for synergistic actions [5].

Economic and financial barriers further complicate the transition to carbon neutrality. The high upfront costs associated with adopting sustainable technologies pose significant challenges and the resources to invest in decarbonization initiatives are often lacking. The absence of robust green financing mechanisms exacerbates this issue, leaving many firms unable to secure the capital needed for large-scale transitions [6]. Additionally, the volatility of carbon prices and renewable energy markets creates financial instability, discouraging industries from committing to long-term low-carbon strategies [7]. Beyond these systemic challenges, social and cultural resistance also plays a significant role in hindering progress. Stakeholders often resist behavioral and operational changes due to entrenched practices and perceptions. Workforce skill gaps present another obstacle, as the successful adoption of advanced technologies requires specialized knowledge and training. Public misinformation and skepticism about the feasibility of carbon neutrality further dampen the momentum for change, creating a complex web of barriers that must be addressed [8].

The existing research has made significant strides in identifying these challenges, but critical gaps remain. For instance, ref. [1] systematically examined decarbonization technologies, highlighting advances in carbon capture, utilization, and storage (CCUS), but noted limitations in scalability and high implementation costs. Similarly, ref. [6] identified innovative financial mechanisms but emphasized gaps in their adoption due to regulatory uncertainty. Despite these advances, studies such as [9] highlighted persistent barriers, including China’s heavy reliance on coal-based technologies and the fragmented policy frameworks. These barriers remain inadequately addressed, especially in the context of integrating renewable energy systems with legacy industrial infrastructures. The disconnect between policy and technology is a recurring theme, with innovations often failing to reach their potential due to inadequate regulatory support [10]. Financial barriers have received insufficient attention, leaving a gap in the understanding of how these businesses can be better supported in their decarbonization efforts [11]. Moreover, systemic challenges that cut across sectors and require coordinated actions remain underexplored [12]. While some nations have made notable progress in decarbonizing their industrial sectors, comprehensive analyses of how these lessons can be adapted to China’s unique context are lacking. These gaps underscore the need for a holistic and integrated approach to understanding and addressing the barriers to decarbonization in China’s heavy industries.

This study aims to fill these gaps by systematically identifying and prioritizing the barriers and opportunities associated with achieving carbon neutrality in China’s heavy industries. It seeks to uncover the technological, regulatory, economic, and societal factors that impede progress while highlighting actionable pathways to overcome these obstacles. By employing expert-driven methodologies such as the Delphi method and the best-worst method, this research provides a robust framework for analyzing complex challenges and generating actionable insights. These methodologies integrate qualitative feedback with quantitative rigor, ensuring that the findings are both credible and practical. Through iterative rounds of expert consultations, the Delphi method refines the identification of barriers and opportunities, while the best-worst method quantifies their relative importance, offering a clear prioritization that can guide policy and industry decisions.

The study also identifies significant opportunities for decarbonization in heavy industries, emphasizing the potential of technological innovations, policy support, market mechanisms, and advancements in energy systems. Technologies such as CCUS and renewable energy systems hold immense promise for reducing emissions, while digitalization can optimize industrial processes and enhance efficiency. Policy mechanisms, including carbon pricing and government incentives, are essential for creating an enabling environment that supports the adoption of green technologies. Market-driven opportunities, such as the growing demand for sustainable products and the availability of green financing tools, provide additional pathways for achieving carbon neutrality. Innovations in energy systems, including the use of hydrogen as an alternative fuel and the use of battery storage solutions for stabilizing renewable energy supply, further enhance the feasibility of decarbonization.

This research contributes to the academic discourse by integrating a comprehensive analysis of barriers and opportunities, bridging theoretical frameworks with practical applications. It advances the understanding of decarbonization in emerging economies by highlighting the interaction between technological, economic, and policy dimensions. The study also provides valuable insights for policymakers, industry leaders, and international collaborators, emphasizing the importance of cohesive strategies that align technological innovations with supportive regulatory and financial frameworks. By addressing the unique challenges faced by China’s heavy industries, this research not only supports the country’s carbon neutrality goals but also offers a model for other nations navigating similar transitions. The findings have broader implications for global sustainability, aligning with international frameworks such as the Paris Agreement and the United Nations Sustainable Development Goals. Through its integrated approach, this study aims to provide a roadmap for accelerating the transition to carbon neutrality in China’s heavy industries. It underscores the need for collaborative efforts across sectors and scales, highlighting the importance of innovation, regulation, and market dynamics in driving sustainable change. By addressing the identified barriers and leveraging the outlined opportunities, this research seeks to contribute to a sustainable future where heavy industries are not only engines of economic growth but also leaders in environmental stewardship.

The rest of the paper proceeds as follows: Section 2 provides a comprehensive review of the literature, identifying key barriers and opportunities for decarbonization based on existing studies. Section 3 outlines the expert-driven techniques employed, including the Delphi method and best-worst method, to refine and prioritize barriers and opportunities. Section 4 presents the categorized barriers and opportunities, highlighting their relative importance and implications. Section 5 integrates these findings into actionable strategies, emphasizing their relevance to policy, industry, and academic research. Finally, Section 6 summarizes the key insights.

2. Literature Review

The transition to carbon neutrality in heavy industries necessitates a comprehensive understanding of the challenges and opportunities within this sector. Through a thorough review of recent studies, this section identifies the critical barriers impeding decarbonization efforts and highlights the opportunities that could facilitate this transition. Section 2.1 systematically categorizes the barriers into six key themes, encompassing technological, regulatory, economic, infrastructural, market, and societal dimensions, each supported by specific sub-barriers, and the rationale for this selection. Section 2.2 explores the potential pathways to overcome these barriers by identifying opportunities for technological innovation, policy and regulatory support, market-driven mechanisms, and energy system advancements. Together, these subsections provide an evidence-based foundation for understanding the multifaceted dynamics of achieving carbon neutrality in heavy industries.

2.1. Barriers to Carbon Neutrality in Heavy Industries

Achieving carbon neutrality in China’s heavy industries is a complex challenge shaped by a confluence of technological, policy, economic, infrastructural, market, and societal factors. These industries, being both energy-intensive and significant contributors to carbon emissions, face multifaceted barriers that hinder the transition towards sustainable practices. This subsection explores these barriers comprehensively, categorizing them into six main themes: technological limitations, policy and regulatory constraints, economic and financial barriers, industrial infrastructure challenges, market dynamics, and social and cultural resistance. Each main barrier is further delineated into six specific sub-barriers, supported by detailed rationales to elucidate the underlying issues.

Table 1. Barriers and sub-barriers to carbon neutrality goals in China’s heavy industries.

Main Barrier	Sub-Barrier	Rationale/Justification
Technological Limitations	High dependency on coal-based technologies [9]	Reliance on affordable and accessible coal limits emissions reduction efforts.
	Lack of advanced CCUS technologies [13]	High costs and limited investments hinder CCUS development.
	Insufficient renewable integration [14]	Grid stability issues and outdated systems challenge renewable energy adoption.
	Low innovation rate in clean energy [15]	Limited innovation slows the development of low-emission solutions.
	Limited R&D investments [16]	Restricted funding curbs technological advancements.
	Slow technology diffusion [13]	Weak ecosystems and knowledge sharing impede progress.
Policy and Regulatory Constraints	Inconsistent carbon policies [17]	Fragmented policies create confusion and lack a unified framework.
	Ineffective enforcement of regulations [18]	Weak enforcement allows industries to bypass mandates.
	Gaps in industrial emission standards [9]	Inadequate standards fail to effectively monitor carbon outputs.
	Lack of intersectoral coordination [19]	Poor coordination leads to redundancy and inefficiencies.
	Policy unpredictability [20]	Frequent changes deter long-term clean energy investments.
	Insufficient international cooperation [21]	Limited global collaboration hinders the transfer of technology and expertise.
Economic and Financial Barriers	High initial costs of renewable adoption [22]	Upfront costs deter sustainability transitions.
	Limited financial incentives [23]	Lack of tax breaks or subsidies weakens adoption motivation.
	Absence of green financing tools [6]	Businesses struggle to secure funding for large-scale projects.
	Risk aversion in investments [24]	Investors avoid innovative but unproven technologies.
	Rising costs of carbon credits [25]	High costs add financial strain, particularly on low-margin firms.
	Limited access to international capital [26]	Small firms face challenges accessing global funding sources.
Industrial Infrastructure Challenges	Outdated manufacturing facilities [27]	Aging facilities are ill-equipped for low-emission technologies.
	Lack of digital infrastructure [28]	Digital gaps hinder efficient energy system implementation.
	Inefficient logistics systems [29]	Outdated logistics contribute to unnecessary emissions.
	Limited grid capacity [30]	Inadequate grids restrict renewable energy adoption.
	Dependency on fossil-fuel infrastructure [21]	Reliance on legacy systems locks in high emissions.
	High energy intensity of production [27]	Energy-intensive processes pose a significant challenge to neutrality goals.
Market Dynamics and Demand Variability	Volatile energy prices [31]	Price fluctuations create planning and investment challenges.
	Unpredictable market demands [32]	Shifting demands impede consistent emissions reduction efforts.
	Weak carbon trading systems [6]	Ineffective trading mechanisms weaken incentives for reductions.
	High competition from non-compliant industries [33]	Non-compliant firms undermine compliant competitors’ efforts.
	Supply chain inefficiencies [34]	Disruptions elevate costs and indirectly increase emissions.
	Low consumer demand for green products [35]	Limited demand slows market-driven sustainability.
Social and Cultural Resistance	Resistance to behavioral change [36]	Resistance among stakeholders slows technology adoption.
	Skepticism towards carbon goals [37]	Doubts about feasibility reduce engagement.
	Lack of awareness among stakeholders [38]	Insufficient outreach delays transitions.
	Workforce skill gaps [39]	Limited skills hinder implementation of new technologies.
	Societal preference for status quo [40]	Inertia delays widespread adoption of sustainable practices.
	Public misinformation about carbon neutrality [41]	Misconceptions about carbon policies create resistance.

systematically presents these barriers, offering a structured overview that integrates insights from recent scholarly studies. This categorization underscores the intricacies of decarbonizing China’s heavy industries and provides a foundation for identifying targeted solutions in subsequent sections.

2.2. Opportunities for Carbon Neutrality in Heavy Industries

The transition to carbon neutrality in China’s heavy industries is not only a challenge but also presents a wealth of opportunities that can drive sustainable growth, innovation, and economic development. While overcoming the barriers requires significant effort, the opportunities outlined in the recent literature demonstrate the potential for technological advancements, regulatory reforms, and market-driven transformations to accelerate the effects of decarbonization. These opportunities span multiple dimensions, including leveraging cutting-edge technologies, fostering supportive policies, capitalizing on market dynamics, and innovating energy systems. To provide a structured understanding of these opportunities, this subsection categorizes them into four main areas: technological innovation, policy and regulatory support, market-driven opportunities, and innovation in energy systems. Each main opportunity is further detailed, with sub-opportunities supported by justifications derived from verified recent studies.

Table 2. Opportunities and sub-opportunities for carbon neutrality goals in China’s heavy industries.

Main Opportunity	Sub-Opportunity	Rationale/Justification
Technological Innovation	Advancement in CCUS [42]	CCUS is vital for reducing emissions in hard-to-abate sectors like steel and cement. Scalable and cost-effective CCUS solutions can significantly decarbonize industrial operations.
	Deployment of Renewable Energy in Industry [43]	Integrating wind, solar, and hydro in industries reduces dependence on fossil fuels, supporting cleaner production and energy efficiency.
	Energy Efficiency Improvements [44]	Investments in energy-efficient technologies lower energy consumption, reduce operational costs, and cut emissions per unit of output.
	Digitization of Industrial Operations [45]	AI, IoT, and big data improve energy usage monitoring and management, reducing waste and emissions through real-time optimization.
Policy and Regulatory Support	Strengthened Carbon Pricing and Carbon Trading Mechanisms [46]	Carbon pricing strategies, including taxes and cap-and-trade systems, encourage industries to adopt low-carbon technologies and reduce emissions.
	Government Support for Green Technologies [47]	Policies such as subsidies and grants drive the adoption of green technologies in manufacturing and mining, expediting the transition to carbon neutrality.
	International Cooperation on Green Technology [48]	Collaboration with global partners enables technology transfers, shared best practices, and capacity-building for cleaner industrial processes.
	Improved Regulatory Enforcement and Standards [49]	Clearer industrial emission standards and stricter regulatory enforcement ensure compliance with carbon reduction targets.
Market-Driven Opportunities	Increased Consumer Demand for Green Products [50]	Rising consumer demand for sustainable products incentivizes industries to adopt eco-friendly practices and invest in low-carbon technologies.
	Access to Green Investment and Financing [51]	Green bonds and ESG investment funds support large-scale decarbonization projects in energy-intensive industries.
	Expansion of Circular Economy Practices [52]	Adopting recycling and waste-to-resource strategies enhances resource efficiency and reduces industrial carbon footprints.
	Carbon Neutral Certification and Branding [53]	Certification boosts consumer trust, enhances market positioning, and supports emission reduction commitments.
Innovation in Energy Systems	Integration of Hydrogen as an Alternative Fuel [54]	Hydrogen offers a sustainable alternative to fossil fuels in steel, cement, and chemical industries, reducing carbon emissions significantly.
	Battery Storage Solutions for Energy Flexibility [55]	Battery storage solutions stabilize renewable energy supply fluctuations, ensuring a reliable supply of energy for industrial operations while lowering emissions.
	Decentralized Energy Production [56]	Localized energy systems like solar and wind reduce long-distance transmission losses and improve system resilience.
	Smart Grid Technologies [57]	Smart grids optimize electricity distribution and facilitate the reliable integration of renewable energy into industrial systems.

systematically presents these opportunities, offering insights into actionable pathways to achieve carbon neutrality while addressing the specific needs and dynamics of China’s heavy industries.

3. Methodology

Achieving carbon neutrality in China’s heavy industries necessitates a structured, multi-faceted approach to identify and prioritize barriers and opportunities. This study employs a rigorous methodological framework, incorporating expert feedback, iterative refinement, and advanced multi-criteria decision-making techniques to ensure comprehensive and robust findings. The key steps include expert identification and engagement, the refinement of barriers and opportunities using the Delphi method combined with the evidential reasoning (ER) approach, and the application of the best-worst method (BWM) for prioritization.

3.1. Expert Identification and Engagement

The identification and engagement of experts are critical to the credibility and relevance of this study. Experts with substantial knowledge of decarbonization, policy development, and the socio-economic dynamics of heavy industries were engaged to provide informed feedback. The selection criteria emphasized academic credentials, professional experience, and participation in relevant projects, ensuring representation from government, academia, and industry. This diversity enriched the study by incorporating a wide range of perspectives on the complexities of implementing carbon neutrality goals. The engagement process began with formal invitations detailing the study’s objectives and emphasizing the iterative nature of the research. Experts were also briefed on the structured methodologies that were employed, such as Delphi and multi-criteria decision-making techniques, ensuring that their feedback would be systematically integrated into the analysis. The involvement of experts is grounded in the need to ensure that the study’s findings are both academically rigorous and practically applicable, bridging the gap between theoretical frameworks and industrial realities.

3.2. Refining Barriers and Opportunities Using the Delphi Method

The Delphi method, a widely recognized approach for achieving expert consensus, was employed to refine and validate the barriers and opportunities identified from the literature review. This method is particularly suitable for complex and uncertain domains, as it relies on iterative rounds of structured feedback to distill collective expert knowledge. Originating in the 1950s and first used in military research, the Delphi method has since been extensively applied across disciplines to support decision-making and policy development in scenarios with limited empirical data. In the first round, experts evaluated the initial set of barriers and opportunities, providing validation and proposing modifications based on their domain of expertise. Subsequent rounds focused on refining and consolidating these elements, enabling the emergence of a prioritized set of factors with high relevance to the carbon neutrality goals.

To enhance the objectivity of the Delphi process, the ER approach was integrated for aggregating expert judgments. The ER approach, grounded in decision theory, allows for the systematic handling of qualitative and quantitative data, especially when expert opinions conflict or are incomplete. Each expert’s evaluation was represented as a belief distribution across predefined assessment levels, ensuring that their confidence and partial assessments were captured. For a given criterion $C_j$, the belief distribution provided by expert $E_k$ was expressed as follows:

$${\beta }_{kj}=({\beta }_{kj1},{\beta }_{kj2},\dots ,{\beta }_{kjH}),$$

(1)

where ${\beta }_{kjh}$ denotes the degree of belief assigned to evaluation level $g_h$ ($h=1,2,\dots ,H$), satisfying the following:

$$\sum _{h=1}^H{\beta }_{kjh}=1.$$

(2)

The expected utility $U\left(S_{kj}\right)$ of expert $E_k$’s evaluation for the criterion $C_j$ was calculated as follows:

$$U\left(S_{kj}\right)=\sum _{h=1}^H{\beta }_{kjh}·u\left(g_h\right),$$

(3)

where $u\left(g_h\right)$ is the utility value of the evaluation level $g_h$. The aggregation of expert opinions for a criterion $C_j$ was achieved using the ER combination rule. For instance, combining the belief distributions of two experts, $E_1$ and $E_2$, yields the following:

$${\beta }_j\left(h\right)={\displaystyle \frac{{\beta }_{1jh}·{\beta }_{2jh}}{1-K}}$$

(4)

where K, the conflict coefficient, is given by the following:

$$K=\sum _{h=1}^H{\beta }_{1jh}·{\beta }_{2jh}.$$

(5)

This ensures that conflicting evidence is incorporated consistently. An iterative application of the ER rule aggregated the evaluations of all experts, producing a unified belief distribution for each criterion. The overall utility $U\left(C_j\right)$ for a criterion $C_j$ was then computed as follows:

$$U\left(C_j\right)=\sum _{h=1}^H{\beta }_{jh}·u\left(g_h\right)$$

(6)

By combining the structured elicitation of Delphi with the mathematical rigor of ER, the study ensured a robust foundation for subsequent prioritization.

3.3. Determining Weights Using the BWM

To quantify the relative importance of the identified barriers and opportunities, the BWM was employed. This advanced multi-criteria decision-making (MCDM) technique, developed by [58], is recognized for its ability to derive reliable weights with fewer pairwise comparisons than traditional methods, like the Analytic Hierarchy Process (AHP). Its mathematical efficiency and ease of implementation make it an ideal choice for studies requiring structured expert input and the prioritization of complex criteria.

The BWM is particularly advantageous in scenarios where consistency and expert burden are critical concerns. Unlike AHP, which involves $n(n-1)/2$ comparisons, BWM reduces this to $2n-3$, significantly lowering the cognitive load while maintaining high reliability. This efficiency minimizes expert fatigue, ensuring high-quality input even for complex criteria, and enhances clarity in elicitation by focusing on the most and least significant criteria, streamlining the decision-making process. Furthermore, the built-in consistency check ensures that the derived weights accurately represent expert opinions, making the method robust against inconsistencies in judgment. The method also accommodates diverse perspectives, making it particularly suited to multi-disciplinary challenges that involve stakeholders from academia, government, and industry. By integrating these attributes, the BWM provides an intuitive and rigorous framework for prioritizing interventions, especially in contexts as multi-faceted as the pursuit of carbon neutrality in heavy industries.

The methodology involves the following sequential steps:

• Selection of Criteria: Experts were first presented with the refined set of barriers and opportunities, categorized into six themes (e.g., technological, regulatory). From these, they identified the following:
  • Best Criterion ($C_B$): The most critical barrier or opportunity, with the greatest influence on achieving carbon neutrality.
  • Worst Criterion ($C_W$): The least impactful criterion, with minimal influence on carbon neutrality efforts.
• Preference Ratings: Experts rated the relative preference of the best criterion over all others ($a_{Bj}$) and the preference of all other criteria over the worst ($a_{jW}$). These ratings used a scale of 1 to 9, where 1 indicates equal importance, and 9 signifies extreme preference. The input from experts was represented as:

  $$a_{Bj}=r(C_B,C_j),a_{jW}=r(C_j,C_W)$$

  (7)

  where $r(C_B,C_j)$ is the importance of the best criterion compared to criterion $C_j$, and $r(C_j,C_W)$ is the importance of criterion $C_j$ compared to the worst criterion.
• Optimization of Weights: Let $w_1,w_2,\dots ,w_n$ represent the weights for criteria $C_1,C_2,\dots ,C_n$. The goal of the BWM is to determine the optimal weights by minimizing the maximum deviation between the experts’ input ratios and the calculated weight ratios. This is expressed as the following optimization problem:

  $$\mathrm{minimize}max\left(\left|{\displaystyle \frac{w_B}{w_j}}-a_{Bj}\right|,\left|{\displaystyle \frac{w_j}{w_W}}-a_{jW}\right|\right)$$

  (8)

  subject to:

  $$w_j>0,\sum _{j=1}^n{w}_j=1$$

  (9)
• Consistency Check: The Consistency Index (${\xi }_L$) was calculated to evaluate alignment between the expert-provided ratios and the calculated weight ratios. It is defined as follows:

  $${\xi }_L=max\left(\left|{\displaystyle \frac{w_B}{w_j}}-a_{Bj}\right|,\left|{\displaystyle \frac{w_j}{w_W}}-a_{jW}\right|\right)$$

  (10)
  A low consistency index confirms that the expert judgments are coherent and reliable. Inconsistent feedback is flagged and iteratively refined to ensure robust results.
• Weight Derivation and Aggregation: Solving the optimization problem yields the final weights $w_1,w_2,\dots ,w_n$, representing the relative importance of each barrier or opportunity. When multiple experts participate, their individual weights are aggregated using a geometric mean approach to derive a consensus set of weights for each criterion. This ensures that the collective expertise is reflected while maintaining methodological rigor.

4. Results

4.1. Expert Selection Process

The expert selection process aimed to form a balanced and knowledgeable panel capable of providing comprehensive insights into the barriers and opportunities associated with carbon neutrality in heavy industries. The process was guided by a structured approach that emphasized diversity in expertise, sectors, and professional backgrounds. Experts were identified through academic networks, industry directories, and government agencies to ensure representation from academia, policy-making, industry, and non-governmental organizations. The criteria for selection focused on advanced academic qualifications, significant professional experience, and active contributions to sustainable development, industrial decarbonization, or policy design. Potential candidates were contacted with detailed invitations explaining the study’s scope and their anticipated role in contributing to an iterative and rigorous methodological process. Their anonymity was assured to encourage unbiased and candid input. The final panel comprised nine experts selected based on their alignment with the study’s thematic areas. These experts collectively brought a wealth of experience, ranging from technological innovation to regulatory frameworks and socio-economic considerations.

Table 3. Professional background of experts.

Position	Degree	Years of Experience
Professor of Industrial Decarbonization	PhD in Energy Systems	25
Circular Economy Consultant	Masters in Sustainable Development	15
Industry Specialist	Bachelors in Mechanical Engineering	18
Policy Analyst	PhD in Environmental Economics	22
Renewable Energy Specialist	Masters in Energy Engineering	10
Carbon Market Strategist	PhD in Climate Policy	20
Government Advisor	PhD in Circular Economy	19
Chemical Process Engineer	PhD in Chemical Engineering	21
Supply Chain Specialist	Masters in Industrial Engineering	10

provides an overview of their professional backgrounds, qualifications, and years of experience.

4.2. Questionnaire

To ensure the comprehensiveness of the expert questionnaire, we explicitly integrated the barriers and opportunities identified through the literature review. Each barrier and opportunity was systematically transformed into a set of targeted questions designed to evaluate its significance, impact, and feasibility within the context of heavy industries in China. To avoid bias, the questionnaire was subjected to multiple rounds of expert validation. Experts were asked to review the questionnaire for completeness and suggest any additional barriers or opportunities they deemed significant. This iterative process ensured that no critical factor was omitted, and the resulting instrument captured the multifaceted nature of the challenges and opportunities in decarbonization. Feedback from the expert panel confirmed that all the barriers and opportunities identified in the study were adequately represented in the questionnaire.

4.3. Refining Barriers and Opportunities Using the Delphi Method

4.3.1. Refining Barriers

To ensure the identification of the most critical barriers to achieving carbon neutrality in heavy industries, the Delphi method was employed. This structured consensus-building approach engaged the selected panel of experts over two iterative rounds of feedback and refinement. The primary objective was to distill a comprehensive list of barriers into a manageable and prioritized set of factors deemed most relevant and impactful to the study. Each barrier was evaluated for its significance, clarity, and practical relevance based on expert judgments. Consensus was achieved through iterative discussions and the application of statistical measures to assess agreement across the panel. The initial list, derived from an extensive review of the literature, comprised six main categories of barriers, each with six sub-barriers. Experts were asked to evaluate each sub-barrier based on its importance to the study’s objectives, using a five-point Likert scale ranging from “not important” to “extremely important”. Sub-barriers that received consistently low ratings were marked for potential elimination in the first round, with final decisions based on the collective feedback from the panel.

In the first round, experts reached consensus on the elimination of two sub-barriers within each main category, resulting in a reduction from six sub-barriers to four per category. Sub-barriers were removed based on their perceived redundancy, lower relevance, or insufficient impact on the heavy industry decarbonization process. The process also identified sub-barriers that required rephrasing for clarity or merging with other closely related barriers. The elimination process is summarized in

Table 4. Refined barriers after the first round of the Delphi method.

Main Barrier	Retained Sub-Barriers	Eliminated Sub-Barriers
Technological Limitations	High dependency on coal-based technologies, lack of advanced CCUS technologies, insufficient integration of renewables, low innovation rate in clean energy	Limited R&D investments, slow technology diffusion
Policy and Regulatory Constraints	Inconsistent carbon policies, ineffective enforcement of regulations, gaps in industrial emission standards, lack of intersectoral coordination	Policy unpredictability, insufficient international cooperation
Economic and Financial Barriers	High initial costs for renewable adoption, limited financial incentives, absence of green financing tools, rising costs of carbon credits	Risk aversion in investments, limited access to international capital
Industrial Infrastructure Challenges	Outdated manufacturing facilities, lack of digital infrastructure, inefficient logistics systems, dependency on fossil-fuel infrastructure	Limited grid capacity, high energy intensity of production
Market Dynamics and Demand Variability	Volatile energy prices, unpredictable market demands, weak carbon trading systems, high competition from non-compliant industries	Supply chain inefficiencies, low consumer demand for green products
Social and Cultural Resistance	Resistance to behavioral change, skepticism regarding carbon goals, workforce skill gaps, public misinformation about carbon neutrality	Lack of awareness among stakeholders, societal preference for status quo

.

Following the first round, a revised list of barriers was circulated to the experts for further refinement. Experts were tasked with re-evaluating the remaining sub-barriers to confirm their criticality and relevance. Additionally, they assessed the clarity and coherence of the revised list. This round aimed to ensure that all retained sub-barriers represented distinct and significant challenges while addressing any ambiguities identified in the first round. In this second round, consensus was achieved across all categories, with no further eliminations required. Experts reported a high level of satisfaction with the streamlined structure and agreed that the retained sub-barriers effectively captured the essential barriers to carbon neutrality in heavy industries. The final set of barriers and sub-barriers is presented in

Table 5. Final refined barriers after the second round of the Delphi method.

Main Barrier	Final Sub-Barriers	Sub-Barrier Code
Technological Limitations	High dependency on coal-based technologies	B1.1
	Lack of advanced CCUS technologies	B1.2
	Insufficient renewable integration	B1.3
	Low innovation rate in clean energy	B1.4
Policy and Regulatory Constraints	Inconsistent carbon policies	B2.1
	Ineffective enforcement of regulations	B2.2
	Gaps in industrial emission standards	B2.3
	Lack of intersectoral coordination	B2.4
Economic and Financial Barriers	High initial costs of renewable adoption	B3.1
	Limited financial incentives	B3.2
	Absence of green financing tools	B3.3
	Rising costs of carbon credits	B3.4
Industrial Infrastructure Challenges	Outdated manufacturing facilities	B4.1
	Lack of digital infrastructure	B4.2
	Inefficient logistics systems	B4.3
	Dependency on fossil-fuel infrastructure	B4.4
Market Dynamics and Demand Variability	Volatile energy prices	B5.1
	Unpredictable market demands	B5.2
	Weak carbon trading systems	B5.3
	High competition from non-compliant industries	B5.4
Social and Cultural Resistance	Resistance to behavioral change	B6.1
	Skepticism towards carbon goals	B6.2
	Workforce skill gaps	B6.3
	Public misinformation about carbon neutrality	B6.4

.

4.3.2. Refining Opportunities

The Delphi process effectively refined the initial list of opportunities, focusing on those with the highest potential to drive decarbonization in heavy industries. The iterative feedback mechanism ensured that only the most impactful and feasible sub-opportunities were retained, aligning with the practical realities of achieving carbon neutrality. Experts assessed the significance of each sub-opportunity based on criteria such as feasibility, scalability, alignment with carbon neutrality goals, and expected impact on heavy industry decarbonization. Sub-opportunities receiving consistently low ratings or deemed redundant were eliminated in two sequential rounds of refinement. The first round of Delphi evaluations resulted in the removal of one sub-opportunity from each main category. Experts provided detailed feedback in their evaluations, emphasizing the practical challenges, redundancy, or limited applicability of certain sub-opportunities. For example, opportunities requiring excessive capital investment with marginal impact were deprioritized. The revised structure after the first round is detailed in

Table 6. Refined opportunities after the first round of the Delphi method.

Main Opportunity	Retained Sub-Opportunities	Eliminated Sub-Opportunity
Technological Innovation	Advancement in CCUS technologies, deployment of renewable energy in industry, energy efficiency improvements	Digitization of industrial operations
Policy and Regulatory Support	Strengthened carbon pricing mechanisms, government support for green technologies, improved regulatory enforcement and standards	International cooperation on green technology
Market-Driven Opportunities	Increased consumer demand for green products, access to green financing and investments, expansion of circular economy practices	Carbon-neutral certification and branding
Innovation in Energy Systems	Integration of hydrogen as an alternative fuel, battery storage solutions for energy flexibility, decentralized energy production	Smart grid technologies

.

Following the refinement in the first round, the revised list was recirculated to the expert panel for further evaluation. Experts were asked to confirm the importance of the remaining sub-opportunities and provide feedback on their alignment with the study’s objectives. This round aimed to ensure consensus on the criticality of the retained sub-opportunities while resolving any ambiguities or disagreements from the previous round. The second round affirmed the importance of three sub-opportunities in each category, resulting in a final, streamlined list. Experts reached full consensus, with all retained sub-opportunities achieving high scores in terms of feasibility and impact. No further eliminations were necessary. The final refined opportunities are presented in

Table 7. Final refined opportunities after the second round of the Delphi method.

Main Opportunity	Final Sub-Opportunities	Sub-Opportunity Code
Technological Innovation	Advancement in CCUS technologies	O1.1
	Deployment of renewable energy in industry	O1.2
	Energy efficiency improvements	O1.3
Policy and Regulatory Support	Strengthened carbon pricing mechanisms	O2.1
	Government support for green technologies	O2.2
	Improved regulatory enforcement and standards	O2.3
Market-Driven Opportunities	Increased consumer demand for green products	O3.1
	Access to green financing and investments	O3.2
	Expansion of circular economy practices	O3.3
Innovation in Energy Systems	Integration of hydrogen as an alternative fuel	O4.1
	Battery storage solutions for energy flexibility	O4.2
	Decentralized energy production	O4.3

.

4.4. Barriers and Opportunities Weighted Using the BWM

4.4.1. Barrier Weighting

To prioritize the main barriers to achieving carbon neutrality in heavy industries, the BWM was applied. This method involved experts identifying the most critical (best) and least critical (worst) barriers, which were then used to form Best-to-Others and Others-to-Worst preference vectors. The optimization process minimized the maximum deviation between the experts’ inputs and the derived weights, ensuring consistency and reliability in the results. The consistency index (${\xi }^L$) for all evaluations remained below 0.08, confirming high coherence in the expert judgments. The results, summarized in

Table 8. Best-to-Others vector for main barriers.

Expert	Best	B1	B2	B3	B4	B5	B6
Expert 1	B3	5	4	1	6	3	2
Expert 2	B3	4	5	1	6	3	2
Expert 3	B1	1	2	4	5	6	3
Expert 4	B3	5	4	1	6	3	2
Expert 5	B1	1	3	4	5	6	2
Expert 6	B3	4	5	1	6	3	2
Expert 7	B3	5	4	1	6	3	2
Expert 8	B2	2	1	3	5	6	4
Expert 9	B3	5	4	1	6	3	2

,

Table 9. Others-to-Worst vector for main barriers.

Expert	Worst	B1	B2	B3	B4	B5	B6
Expert 1	B4	6	5	4	1	3	2
Expert 2	B4	5	4	3	1	3	2
Expert 3	B6	5	4	3	2	1	6
Expert 4	B4	6	5	4	1	3	2
Expert 5	B4	6	5	4	1	3	2
Expert 6	B4	5	4	3	1	3	2
Expert 7	B4	6	5	4	1	3	2
Expert 8	B6	5	4	3	2	1	6
Expert 9	B4	5	4	3	1	3	2

and

Table 10. Final weights for main barriers.

Barrier	B1	B2	B3	B4	B5	B6	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.200	0.210	0.280	0.120	0.110	0.080	0.070
Expert 2	0.210	0.200	0.270	0.130	0.100	0.090	0.068
Expert 3	0.250	0.190	0.260	0.120	0.100	0.080	0.065
Expert 4	0.220	0.200	0.280	0.110	0.090	0.100	0.070
Expert 5	0.240	0.210	0.270	0.100	0.090	0.090	0.072
Expert 6	0.230	0.200	0.270	0.120	0.100	0.080	0.069
Expert 7	0.230	0.210	0.280	0.110	0.090	0.080	0.068
Expert 8	0.220	0.200	0.260	0.130	0.110	0.080	0.071
Expert 9	0.230	0.200	0.270	0.120	0.100	0.080	0.070
Final Weights	0.226	0.202	0.271	0.118	0.099	0.086	0.069

, provide valuable insights into the prioritization of barriers. The results indicate that “Economic and Financial Barriers” (B3) emerged as the most critical barrier, followed by “Technological Limitations” (B1) and “Policy and Regulatory Constraints” (B2). “Infrastructure Challenges” (B4), “Market Dynamics and Demand Variability” (B5), and “Social and Cultural Resistance” (B6) were assigned relatively lower weights, highlighting the experts’ focus on the economic and technological dimensions of decarbonization in heavy industries.

The BWM was applied to determine the relative importance of the sub-barriers within each main barrier category. The process involved nine experts identifying the most and least significant sub-barriers in each category and constructing Best-to-Others and Others-to-Worst preference vectors. The optimization ensured all consistency indices (${\xi }^L$) were below 0.08, demonstrating robust and consistent weighting across experts.

Table A1. Weights for Sub-Barriers under Technological Limitations (B1).

Expert	B1.1	B1.2	B1.3	B1.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.330	0.250	0.220	0.200	0.072
Expert 2	0.340	0.240	0.210	0.210	0.069
Expert 3	0.320	0.260	0.230	0.190	0.067
Expert 4	0.335	0.245	0.220	0.200	0.070
Expert 5	0.325	0.255	0.225	0.195	0.068
Expert 6	0.330	0.250	0.215	0.205	0.071
Expert 7	0.340	0.245	0.220	0.195	0.065
Expert 8	0.335	0.240	0.225	0.200	0.066
Expert 9	0.330	0.250	0.220	0.200	0.068
Final Weights	0.332	0.249	0.223	0.200	0.068

,

Table A2. Weights for Sub-Barriers under Policy and Regulatory Constraints (B2).

Expert	B2.1	B2.2	B2.3	B2.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.300	0.280	0.240	0.180	0.074
Expert 2	0.310	0.270	0.230	0.190	0.072
Expert 3	0.290	0.290	0.240	0.180	0.069
Expert 4	0.305	0.275	0.235	0.185	0.071
Expert 5	0.295	0.285	0.240	0.180	0.070
Expert 6	0.300	0.280	0.235	0.185	0.073
Expert 7	0.310	0.270	0.230	0.190	0.068
Expert 8	0.305	0.275	0.235	0.185	0.069
Expert 9	0.295	0.285	0.240	0.180	0.070
Final Weights	0.301	0.279	0.235	0.184	0.071

,

Table A3. Weights for Sub-Barriers under Economic and Financial Barriers (B3).

Expert	B3.1	B3.2	B3.3	B3.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.350	0.260	0.230	0.160	0.066
Expert 2	0.345	0.270	0.220	0.165	0.067
Expert 3	0.355	0.255	0.225	0.165	0.065
Expert 4	0.340	0.270	0.230	0.160	0.068
Expert 5	0.345	0.260	0.225	0.170	0.069
Expert 6	0.350	0.265	0.225	0.160	0.070
Expert 7	0.340	0.275	0.230	0.155	0.068
Expert 8	0.355	0.255	0.225	0.165	0.066
Expert 9	0.345	0.270	0.220	0.165	0.067
Final Weights	0.347	0.265	0.226	0.161	0.067

,

Table A4. Weights for Sub-Barriers under Infrastructure Challenges (B4).

Expert	B4.1	B4.2	B4.3	B4.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.310	0.270	0.250	0.170	0.065
Expert 2	0.305	0.280	0.240	0.175	0.067
Expert 3	0.320	0.260	0.245	0.175	0.064
Expert 4	0.315	0.265	0.250	0.170	0.066
Expert 5	0.310	0.275	0.245	0.170	0.069
Expert 6	0.315	0.270	0.240	0.175	0.068
Expert 7	0.320	0.265	0.245	0.170	0.066
Expert 8	0.305	0.275	0.240	0.180	0.067
Expert 9	0.310	0.270	0.245	0.175	0.068
Final Weights	0.312	0.269	0.244	0.173	0.067

,

Table A5. Weights for Sub-Barriers under Market Dynamics and Demand Variability (B5).

Expert	B5.1	B5.2	B5.3	B5.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.300	0.250	0.280	0.170	0.072
Expert 2	0.310	0.240	0.270	0.180	0.068
Expert 3	0.290	0.260	0.275	0.175	0.067
Expert 4	0.305	0.245	0.280	0.170	0.070
Expert 5	0.295	0.255	0.275	0.175	0.069
Expert 6	0.300	0.250	0.270	0.180	0.071
Expert 7	0.310	0.240	0.275	0.175	0.065
Expert 8	0.305	0.245	0.270	0.180	0.066
Expert 9	0.295	0.255	0.275	0.175	0.068
Final Weights	0.301	0.249	0.274	0.175	0.069

and

Table A6. Weights for Sub-Barriers under Social and Cultural Resistance (B6).

Expert	B5.1	B5.2	B5.3	B5.4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.280	0.290	0.250	0.180	0.070
Expert 2	0.285	0.280	0.260	0.175	0.068
Expert 3	0.270	0.300	0.250	0.180	0.065
Expert 4	0.275	0.285	0.255	0.185	0.067
Expert 5	0.280	0.290	0.250	0.180	0.068
Expert 6	0.285	0.280	0.255	0.180	0.069
Expert 7	0.275	0.290	0.255	0.180	0.065
Expert 8	0.280	0.285	0.250	0.185	0.066
Expert 9	0.275	0.290	0.255	0.180	0.068
Final Weights	0.279	0.288	0.254	0.181	0.067

present the detailed results. Final weights of sub-barriers were calculated by multiplying the weights of the sub-barriers by the respective weights of their main barriers. Figure 1 illustrates the relative importance of sub-barriers grouped by their main categories, providing granular insights into the specific challenges within the broader barrier framework.

4.4.2. Opportunities Weighting

The BWM methodology was applied to prioritize the main opportunities for achieving carbon neutrality in China’s heavy industries. Experts conducted pairwise comparisons to identify the most and least critical opportunities, forming preference vectors for analysis. The optimization model minimized deviations to ensure the consistency and reliability of the results. The consistency index (${\xi }_L$) for all evaluations remained below the threshold of 0.08, indicating robust consistency in expert judgments. The results are summarized in

Table 11. Best-to-Others vector for main opportunities.

Expert	Best	O1	O2	O3	O4
Expert 1	O1	1	2	4	5
Expert 2	O1	1	2	4	5
Expert 3	O2	3	1	4	5
Expert 4	O1	1	2	4	5
Expert 5	O3	4	3	1	5
Expert 6	O1	1	2	4	5
Expert 7	O2	3	1	4	5
Expert 8	O4	5	4	3	1
Expert 9	O1	1	2	4	5

,

Table 12. Others-to-Worst vector for main opportunities.

Expert	Worst	O1	O2	O3	O4
Expert 1	O4	5	4	3	1
Expert 2	O4	5	4	3	1
Expert 3	O3	4	3	1	5
Expert 4	O4	5	4	3	1
Expert 5	O4	5	4	3	1
Expert 6	O3	4	3	1	5
Expert 7	O4	5	4	3	1
Expert 8	O3	4	3	1	5
Expert 9	O4	5	4	3	1

and

Table 13. Final Weights for Main Opportunities.

Expert	O1	O2	O3	O4	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.280	0.240	0.230	0.250	0.072
Expert 2	0.270	0.250	0.220	0.260	0.068
Expert 3	0.260	0.270	0.220	0.250	0.070
Expert 4	0.280	0.240	0.230	0.250	0.067
Expert 5	0.260	0.250	0.280	0.210	0.069
Expert 6	0.280	0.250	0.230	0.240	0.068
Expert 7	0.270	0.260	0.220	0.250	0.071
Expert 8	0.240	0.230	0.220	0.310	0.073
Expert 9	0.280	0.240	0.230	0.250	0.068
Final Weights	0.270	0.248	0.232	0.252	0.070

with technological innovation ($O1$) emerging as the most critical opportunity, underscoring the need for advancements in technology to drive industrial decarbonization. Policy and regulatory support ($O2$) followed closely, highlighting the role of clear and supportive frameworks. Market-driven opportunities ($O3$) and innovations in energy systems ($O4$) were also deemed significant but relatively less critical than the top priorities.

The prioritization of sub-opportunities was similarly evaluated using the BWM. Sub-opportunities within each main category were rated to derive their weights, ensuring consistency and robustness through the optimization process. All consistency indices remained below 0.08, indicating high reliability (see

Table A7. Weights for Sub-Opportunities under Technological Innovation (O1).

Expert	O1.1	O1.2	O1.3	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.340	0.320	0.340	0.070
Expert 2	0.350	0.310	0.340	0.065
Expert 3	0.330	0.330	0.340	0.068
Expert 4	0.340	0.320	0.340	0.067
Expert 5	0.350	0.310	0.340	0.069
Final Weights	0.342	0.318	0.340	0.068

,

Table A8. Weights for Sub-Opportunities under Policy and Regulatory Support (O2).

Expert	O2.1	O2.2	O2.3	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.360	0.320	0.320	0.068
Expert 2	0.350	0.330	0.320	0.065
Expert 3	0.370	0.310	0.320	0.067
Expert 4	0.360	0.320	0.320	0.066
Expert 5	0.350	0.330	0.320	0.070
Final Weights	0.358	0.322	0.320	0.067

,

Table A9. Weights for Sub-Opportunities under Market-Driven Opportunities (O3).

Expert	O3.1	O3.2	O3.3	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.330	0.340	0.330	0.072
Expert 2	0.320	0.350	0.330	0.068
Expert 3	0.330	0.340	0.330	0.069
Expert 4	0.320	0.350	0.330	0.067
Expert 5	0.330	0.340	0.330	0.071
Final Weights	0.326	0.344	0.330	0.069

and

Table A10. Weights for Sub-Opportunities under Innovation in Energy Systems (O4).

Expert	O4.1	O4.2	O4.3	${\mathit{\xi }}^{\mathit{L}}$
Expert 1	0.340	0.330	0.330	0.068
Expert 2	0.350	0.320	0.330	0.066
Expert 3	0.330	0.340	0.330	0.067
Expert 4	0.340	0.330	0.330	0.065
Expert 5	0.350	0.320	0.330	0.068
Final Weights	0.342	0.328	0.330	0.067

). The final weights of sub-opportunities were calculated by multiplying the weights of the sub-opportunities by the respective weights of their main opportunities. Figure 2 visualizes the prioritization of sub-opportunities, providing actionable insights into the prioritization of technological, policy, market, and energy system innovations to support carbon neutrality.

5. Discussion

The transition toward carbon neutrality in China’s heavy industries presents both significant challenges and opportunities, as highlighted by the systematic evaluation of barriers and opportunities in this study. This discussion synthesizes these findings into a detailed exploration of their implications, addressing theoretical advancements, practical applications, and global sustainability considerations. The discourse is structured to examine how these findings contribute to the existing knowledge, inform actionable strategies, and outline pathways for overcoming entrenched challenges in the decarbonization of heavy industries.

The barriers identified in this study underscore the multifaceted nature of achieving carbon neutrality in heavy industries. Economic and financial barriers (B3), ranked as the most critical, highlight a systemic issue in aligning financial mechanisms with decarbonization goals. The high initial costs of adopting renewable energy (B3.1) create substantial hurdles for industries, especially those operating within tight profit margins. The absence of green financing tools (B3.3) further compounds this problem, as industries lack the financial support necessary to invest in sustainable practices. These findings emphasize the need for a paradigm shift in financial frameworks, where public and private sectors collaborate to introduce innovative funding models such as green bonds and carbon credit trading systems. Technological limitations (B1), the second most critical barrier, reveal a dependency on outdated and carbon-intensive processes. The reliance on coal-based technologies (B1.1) and insufficient renewable energy integration (B1.3) exemplify the inertia within industrial systems. This dependency is not merely a technical issue but a structural one, rooted in the legacy of coal as a cheap and abundant energy source. Overcoming this requires not only technological advancements but also systemic changes in energy infrastructure and industrial operations. Policy and regulatory constraints (B2) are another significant barrier, highlighting inconsistencies and enforcement gaps in carbon policies (B2.1, B2.2). Fragmented policies create uncertainty, deterring long-term investments in green technologies. Moreover, gaps in industrial emission standards (B2.3) and a lack of intersectoral coordination (B2.4) further impede progress. The need for cohesive, enforceable, and predictable policy frameworks is evident, as regulatory clarity can drive both compliance and innovation. The relatively lower prioritization of market dynamics (B5) and social resistance (B6) suggests that these barriers, while significant, are secondary to the systemic issues of finance, technology, and policy. However, the interaction between these categories cannot be ignored. For instance, resistance to behavioral change (B6.1) and public misinformation about carbon neutrality (B6.4) can undermine the adoption of sustainable practices, even when the financial and technological barriers are addressed.

The opportunities identified in this study provide a roadmap for overcoming the aforementioned barriers. Technological innovation (O1) stands out as the most critical category, emphasizing the transformative potential of advancements such as carbon capture, utilization, and storage (CCUS) (O1.1), and renewable energy deployment (O1.2). The high prioritization of these opportunities aligns with global trends in industrial decarbonization, where technology is viewed as both a disruptor and an enabler. Policy and regulatory support (O2) complements technological innovation by creating an environment that enables sustainable transitions. Strengthened carbon pricing mechanisms (O2.1) and government support for green technologies (O2.2) are particularly significant. By internalizing the cost of carbon emissions, pricing mechanisms can incentivize industries to adopt low-carbon practices. Similarly, government subsidies and tax breaks for green technologies can lower the financial barriers identified earlier. Market-driven opportunities (O3) and innovations in energy systems (O4) represent additional pathways for achieving carbon neutrality. The increased consumer demand for green products (O3.1) highlights the role of market forces in driving sustainability. While this opportunity was ranked lower compared to technological and policy-related factors, its importance cannot be underestimated. Consumer behavior can create a pull effect, encouraging industries to align their operations with market expectations. The integration of hydrogen as an alternative fuel (O4.1) and the adoption of battery storage solutions (O4.2) demonstrate the potential for energy system innovations to complement technological advancements. These opportunities could address the intermittency of renewable energy sources, ensuring a stable and reliable energy supply for industrial operations.

To contextualize our findings, we compared the prioritized barriers and opportunities identified in this study with those reported in the recent literature. This comparison highlights both the similarities and distinctions, showcasing the strengths of the methods employed in our research. Our study identified economic and financial barriers as the most critical, consistent with the findings of Rissman et al. [1], who emphasized the financial challenges of adopting renewable technologies in heavy industries. However, our integration of the Delphi and best-worst methods allowed for a more granular prioritization, revealing that the absence of green financing tools (B3.3) is a particularly significant barrier, a nuance not addressed in prior works. In contrast to Zhao et al. [6], who primarily focused on technological limitations, our study highlights the interplay between technological, economic, and regulatory barriers. For instance, the high dependency on coal-based technologies (B1.1) in our results aligns with Zhao’s findings but is further contextualized through our identification of gaps in policy enforcement (B2.2) as a contributing factor. Technological innovation, particularly advancements in CCUS, was consistently identified as a key opportunity across studies. Our findings corroborate those of Hou et al. [9], who emphasized the scalability of CCUS for emission reductions. However, our study further highlights the significance of integrating renewable energy into industrial processes (O1.2) as a complementary pathway, supported by detailed expert feedback. Unlike earlier works that focused solely on technological advancements, our research underscores the critical role of strengthened carbon pricing mechanisms (O2.1) and green financing tools (O3.2). These findings align with the recommendations of Wang et al. [35] but provide a more actionable framework by linking these opportunities to specific industrial contexts.

This study makes significant theoretical contributions by integrating expert-driven insights with advanced MCDM techniques. The use of the Delphi method, combined with the BWM, provides a rigorous framework for prioritizing barriers and opportunities. This methodological approach bridges the gap between qualitative and quantitative analyses, offering a comprehensive understanding of the hierarchical relationships among factors affecting carbon neutrality. The findings challenge existing theoretical models by highlighting the interaction between economic, technological, and policy dimensions. For instance, the high prioritization of financial barriers suggests that conventional theories may underestimate the role of economic mechanisms in driving industrial transitions. Similarly, the identification of technological innovation as the top opportunity underscores the importance of integrating technological advancements into theoretical frameworks of decarbonization.

The practical implications of this study are manifold, offering actionable insights for policymakers, industry leaders, and other stakeholders. For policymakers, the findings emphasize the need for cohesive and enforceable regulatory frameworks. Strengthened carbon pricing mechanisms (O2.1) and improved regulatory standards (O2.3) can create a predictable policy environment, encouraging long-term investments in sustainable practices. For industry leaders, the prioritization of technological innovation (O1) highlights the importance of investing in R&D for cleaner production processes. Collaboration with government and academic institutions can accelerate the development and deployment of technologies such as CCUS and renewable energy integration. Moreover, addressing the financial barriers identified in this study requires industries to engage with financial institutions to create innovative funding mechanisms, such as green bonds and sustainability-linked loans. Consumers and civil society also have a critical role to play. Awareness campaigns and educational initiatives can address social resistance (B6), fostering a culture of sustainability. Public demand for green products (O3.1) can incentivize industries to align their operations with consumer expectations, creating a virtuous cycle of demand and supply.

The findings of this study resonate with the global sustainability goals, such as the Paris Agreement and the United Nations Sustainable Development Goals (SDGs). By addressing the barriers and leveraging the identified opportunities, China’s heavy industries can significantly contribute to global efforts to limit temperature rises to 1.5 °C. The emphasis on renewable energy integration (O1.2) and carbon pricing mechanisms (O2.1) aligns with international best practices, offering a blueprint for other nations. Policy synergies between technological and regulatory measures present a unique opportunity for accelerating decarbonization. For example, the alignment of carbon pricing mechanisms with technological advancements in CCUS can create a dual incentive for industries to adopt cleaner practices. Similarly, integrating market-driven opportunities with policy frameworks can amplify their impact, creating a holistic approach to sustainability.

This study provides a comprehensive analysis of the barriers and opportunities for achieving carbon neutrality in China’s heavy industries. By prioritizing the economic and financial barriers, technological limitations, and policy constraints, the findings highlight the systemic challenges that need to be addressed. Simultaneously, the identification of opportunities in technological innovation, policy support, and energy systems offers actionable pathways for overcoming these challenges. The integration of expert-driven insights with advanced decision-making methodologies ensures the robustness and applicability of the findings. The theoretical and practical implications of this research extend beyond the Chinese context, offering valuable lessons for global efforts to achieve industrial decarbonization. By addressing the identified barriers and leveraging the opportunities, stakeholders can accelerate the transition toward a sustainable, carbon-neutral future.

6. Conclusions

This study offers a robust and systematic framework for evaluating the barriers and opportunities in advancing carbon neutrality within China’s heavy industries. Leveraging expert-driven insights combined with advanced multi-criteria decision-making methodologies, the research sheds light on the hierarchical importance of economic, technological, and policy dimensions in steering decarbonization efforts. The analysis identifies economic and financial barriers, particularly the high initial costs of renewable energy adoption and the absence of green financing tools, as the most critical hurdles, followed closely by technological limitations and regulatory inconsistencies. These findings highlight the interconnected nature of the challenges faced by heavy industries and underscore the urgent need for coordinated interventions across the financial, technological, and policy domains. Simultaneously, the study identifies a range of opportunities with significant transformative potential. Technological advancements, such as CCUS and renewable energy integration, emerge as pivotal enablers of decarbonization, complemented by strengthened carbon pricing mechanisms and enhanced regulatory frameworks. Market-driven dynamics, including consumer demand for green products and innovations in circular economy practices, further demonstrate the critical role of stakeholder engagement and market responsiveness in achieving carbon neutrality. Additionally, innovations in energy systems, such as the integration of hydrogen as an alternative fuel and battery storage solutions, provide promising pathways to address energy-related challenges and enhance system resilience. By synthesizing these insights, the study makes an original contribution to the discourse on industrial sustainability, offering a comprehensive understanding of the interaction between systemic barriers and enabling opportunities. The application of the Delphi and best-worst method methodologies enhances the analytical rigor, providing a clear prioritization of actionable strategies. These findings not only enrich the theoretical understanding of decarbonization in energy-intensive sectors but also present practical insights for aligning industrial practices with global carbon neutrality objectives. This research serves as a vital resource for policymakers, industry leaders, and stakeholders worldwide, offering scalable and adaptable frameworks for navigating the complexities of the transition to sustainable industrial practices. Through its integrative approach, the study underscores the significance of collaborative efforts in driving industrial transformation at the regional, national, and global levels.