Abstract Against the backdrop of frequent adjustments and iterations in global climate policies, the issue of policy uncertainty surrounding corporate energy structure upgrades has become increasingly prominent. A key concern for achieving global green sustainable development is how to efficiently advance corporate low-carbon transition. In view of this, we construct the energy structure low-carbon transition at the enterprise level, and explore the influence and mechanism of climate policy uncertainty on the energy structure low-carbon transition of enterprises from the perspective of enterprise willingness and ability. The research findings indicate: (1) Corporate energy structure low-carbon transition is substantially impeded by climate policy uncertainty, and this conclusion is upheld by a battery of robustness and endogeneity analyses. (2) Climate policy uncertainty inhibits corporate energy structure low-carbon transition by reducing management’s long-term behavior, lowering green technology innovation levels, and weakening effective investment. (3) According to heterogeneity analysis, non-state-owned businesses, areas with lax environmental regulations, and businesses with poor climate risk awareness are more affected by the inhibiting impact caused by climate policy uncertainty. In addition to offering theoretical underpinnings and helpful advice for governments looking to create stable climate policies and enhance climate governance systems, this paper gives fresh perspectives on the fundamental reasoning behind corporate energy structure decarbonization. 1. Introduction Driven by both human activities and natural factors, the process of global warming is accelerating, with glacial retreat becoming increasingly evident and extreme weather events occurring more frequently. Climate instability [ 1] is evolving in an increasingly frequent and intense manner, and global ecosystems are facing profound changes. According to data from the World Meteorological Organization, the past decade has been the hottest on record, with atmospheric carbon dioxide concentrations surpassing historical peaks. The severity of climate change has prompted heightened global concern. According to the “State of the Global Climate Report” released by the World Meteorological Organization, 2024 has been confirmed to be the hottest year since meteorological records began, with the global average temperature being 1.55 °C higher than the pre-industrial level. Entering 2026, global warming and extreme weather have worsened, affecting ecosystems, economies, and energy systems. In response to this global challenge, the international community has established a global climate governance system through multilateral policy frameworks like the Paris Agreement and the Glasgow Climate Pact. It promotes differentiated policies among countries [ 2] to address various kinds of climatic issues by enhancing energy structures and decreasing carbon emissions. China is the globe’s biggest developing nation and specifically presented the objectives of “carbon peaking” and “carbon neutrality” at the United Nations General Assembly in 2020, positioning green development as the core direction of economic transition and driving a comprehensive green transition of society and the economy. According to the National Strategy for Adapting to Climate Change 2035, China has established a multi-level regional climate adaptation framework through a variety of measures, including improving the coordination mechanism for adapting to climate change, promoting a clean and low-carbon transition to the energy delivery system, strengthening fiscal, financial, and technological support, and enhancing ability building. However, in the context of green development, climate policy design and implementation are subject to a variety of restrictions. The actualization of climate legislation must balance multiple demands, including economic development [ 3] and energy stability [ 4], while pursuing energy efficiency and lowering carbon dioxide objectives. The potential contradictions and conflicts between these objectives significantly increase the uncertainty of climate policies, making it difficult to achieve dynamic equilibrium in policy formulation and adjustment. Since enterprises are the main energy users, the unpredictable nature of the political environment also significantly affects the economic activity of corporations. Energy consumption structure adjustments [ 5, 6] face more variables, while energy structure transition and upgrading are constrained to varying degrees. Uncertainties in climate policy necessarily provide hurdles towards converting to a energy structure low-carbon transition, which is the main route promoting industrial structure upgrading and attaining sustainable development. The dynamic adjustments and frequent revisions of climate policies, coupled with information asymmetry between enterprises and policymakers, lead to errors in enterprises’ interpretations of policy signals. These errors directly disrupt the allocation of resources for enterprises’ green and low-carbon projects, particularly in core elements such as capital, technology, and human resources, therefore slowing businesses’ low-carbon transition. Furthermore, local governments may frequently adjust and reduce subsidies for renewable energy based on short-term performance goals, which strongly impacts corporate innovation in low-carbon technology, exacerbates R&D cost challenges, leads to the suspension or regression of technology, and severely weakens the intrinsic motivation for corporate low-carbon transition. Additionally, policy adjustments often have lengthy transition periods. When policy adjustments are too drastic or lack adequate notice and buffer mechanisms, enterprises struggle to complete adaptive adjustments such as production equipment upgrades, process optimization, and staff training within a short timeframe, leading to survival crises for some enterprises. To maintain basic operations, companies are forced to allocate their primary resources and efforts to addressing survival issues, leaving little time or ability for strategic planning and implementation of energy structure low-carbon transition. This, in turn, slows down the overall industry’s energy structure low-carbon transition and hinders the nation’s progress toward optimizing and upgrading its energy structure. The existing literature has established a multi-level research framework. From a macroeconomic perspective, researchers primarily focus on the energy structure changes brought about by climate policy uncertainty. As the world’s energy crisis deepens and the dominance of climate policies grows, the public has begun to replace traditional energy policies with green resources [ 7]. Meanwhile, the rapid development of the green market has also exerted an intangible impact on the traditional energy industry [ 8]. Considering the sustainable development of the economic environment under climate policy uncertainty, governments have also begun to re-examine energy consumption issues, emphasizing energy utilization efficiency [ 9, 10] and the creation and utilization of renewable energy [ 11]. The time lag in climate policies across different industries has also led to significant differences in energy structures, particularly in high-energy-consuming sectors such as power and chemicals [ 12]. Due to characteristics such as high technical iteration costs and significant sunk costs in existing ability, the transition to low-carbon practices in these sectors lags behind other industries. At the micro level, researchers primarily focus on issues such as corporate financial risks arising from climate policy uncertainty [ 13], management inefficiencies related to green transition challenges [ 14], and ESG performance [ 15]. Neoclassical economic theory posits that climate policy uncertainty raises business costs, imposes financing constraints, diverts corporate R&D investments, and hinders green technology innovation [ 16]. Dynamic mechanism studies indicate that climate policy uncertainty exhibits a nonlinear threshold effect with environmental taxes [ 17] and induces discontinuous shocks to specific renewable energy sources through time-varying causal pathways [ 18], while forming a dynamic risk overflow network with traditional energy sources and green markets. This shows that current research still has deficiencies in terms of micro-mechanisms and specific influence paths, and there are still some limitations in the theoretical framework: first, empirical analyses primarily rely on national or prefectural panel data, lacking an examination of the micro-level mechanisms of corporate energy decision-making. As the primary consumers of energy, the optimization of corporate energy consumption structures is a critical component in achieving national energy transition goals. This macro-level perspective may obscure the differentiated response strategies of enterprises in an environment of policy uncertainty. Second, the existing literature mainly concentrates on low-carbon transition implementation paths [ 19] and energy structure maintenance strategies [ 20]. How climate policy uncertainty affects corporate energy systems’ low-carbon transition is little understood. This paper first theoretically explores how climate policy uncertainty affects enterprises’ low-carbon energy structure, then empirically investigates this using data from Chinese A-share listed companies from 2007 to 2022. The findings provide theoretical support for enterprises to better address climate policy uncertainty, and provide scientific evidence for relevant climate policy institutional design and decision-making effectiveness. This work makes these modest contributions: First, considering the varying effects among individual enterprises, we innovatively combine the provincial climate policy uncertainty index with firm environmental scores to construct a climate policy shock variable at the firm level, breaking through the limitations of traditional macro policy models. Second, it has enriched the existing literature. Unlike the limitations of existing studies, which primarily adopt a macro perspective, we examine the complex relationship between climate policy uncertainty and the energy structure low-carbon transitions from the perspectives of firms’ “willingness” and “ability,” providing a detailed analysis of these dynamics in firm-level practice. Third, it investigates the three mechanisms: exacerbating financing constraints, reducing green technology innovation levels, and weakening effective investment. It also conducts heterogeneity analysis in terms of corporate nature, environmental regulation intensity, and corporate climate risk concern, providing new empirical evidence for enterprises to intensify efforts in their energy structure upgrades and low-carbon transitions. 2. Theoretical Analysis and Research Hypothesis 2.1. The Impact of Climate Policy Uncertainty on the Low-Carbon Transition of Enterprises’ Energy Structure Regional disparities in climate change and governments’ climate risk perceptions affect climate policy uncertainty. This can cause market signals to become confused and can show up as crises and difficulties in the corporate energy structures’ transition to low-carbon. The essence of the uncertainty in climate policies refers to the situation where the government, in the process of formulating, adjusting and implementing climate policies such as carbon emission control [ 21, 22], renewable energy subsidies [ 23], and carbon market mechanisms, encounters issues such as ambiguous rules, changes in direction, or unclear implementation strength. The core attribute of this uncertainty is the unpredictability of the future state of policies. The low-carbon transition of corporate energy frameworks, essential for attaining sustainable development, necessitates a systemic change from conventional energy sources such as coal and oil to renewable energy sources like solar and wind power. This transition encompasses multi-dimensional strategic adjustments, including the iterative upgrading of production equipment, investments in cutting-edge technology R&D, and the restructuring of supply chain systems [ 24]. Due to its lengthy investment return cycles and substantial sunk costs, it is profoundly dependent on a stable and predictable policy environment for support. From a micro perspective of corporate operations, companies must continuously adjust their production, investment, energy use, and labor supply [ 25] to adapt to evolving climate policies, which inevitably increases compliance costs. Meanwhile, financial markets, guided by risk aversion principles [ 26], demand stricter collateral terms from businesses, leading to higher financing costs for low-carbon transitions and weakening businesses’ willingness to invest in green initiatives. Frequent climate policy changes limit corporate finance and increase market rivalry, obscuring green technology innovation. Businesses become less technologically capable, making them unable to sustain operational stability when faced with external technology impediments. Additionally, based on the theory of real options [ 27], policy uncertainty grants companies a certain “waiting option,” differing from traditional evaluation methods. The value of real options is typically higher, prompting companies to adopt a wait-and-see strategy to avoid potential asset depreciation. Effective investment is hindered by a cycle of decision delay, insufficient investment, and transition lag, hindering organizations’ green and low-carbon development strategy. Consequently, we propose that: H1:Climate policy uncertainty will inhibit the transition to a low-carbon energy structure for businesses. 2.2. The Mechanism by Which Climate Policy Uncertainty Affects Corporate Energy Structures’ Low-Carbon Transition The uncertainty of climate policy inhibits the “willingness” of low-carbon transition of enterprise energy structure by reducing the long-term behavior of management. Managerial long-termism is embodied in the attention and commitment of decision makers to the long-term value creation of enterprises in strategic planning and resource allocation, and it is the core subjective basis for promoting enterprises to carry out long-term and deferred return strategic changes. Energy structure low-carbon transition involves multi-dimensional adjustment, such as production equipment iteration, clean energy access, supply chain reconstruction, etc., which is characterized by long cycle, high sunk cost and delayed return. The payback period of investment usually exceeds the tenure of managers, so it relies heavily on the stable long-term strategic vision and continuous subjective commitment of management. However, the increasing uncertainty of climate policy is distorting the time preference structure of management, systematically eroding its long-term behavior foundation, and then suppressing the internal will of enterprises to promote low-carbon transition. From the perspective of management’s cognitive decision-making, based on the principal-agent theory, managers face the dual constraints of term assessment pressure and professional reputation, and the length of their decision-making vision is closely related to the stability of the external policy environment [ 28]. When the uncertainty of climate policy rises significantly, it is difficult for enterprises to form a stable long-term income expectation, and the pressure on shareholders to assess the short-term financial performance of management is further amplified. Based on career concerns, managers tend to give priority to limited decision-making attention to verifiable and quantifiable short-term financial indicators during their term of office, but they lack the subjective motivation to continuously invest in the strategic issue of a low-carbon transition with long return period and uncontrollable results, and the long-term behavior is systematically weakened. At the level of strategic commitment selection, the uncertainty of climate policy leads to a significant distortion of management’s time preference. Faced with the high ambiguity of the policy direction, it is difficult for management to judge whether the long-term strategic investment currently made for low-carbon transformation can achieve the expected return in the future policy environment. Based on the risk aversion motivation, management will actively shrink the long-term strategic agenda and pursue short-term projects with quick results and controllable risks [ 29] to ensure the stability of performance during the term of office. The weakening of management’s long-term doctrine directly weakens the subjective driving force of the low-carbon transition of the enterprise energy structure. Low-carbon transition is essentially a long-term strategic behavior that requires management’s continuous commitment at the cognitive level and continuous investment at the resource level. When the uncertainty of climate policy leads management to pay attention to the short-term and reduce strategic vision, even if enterprises have corresponding technical reserves and resource conditions, they will fall into strategic inertia due to insufficient subjective willingness to transform, which will delay the evolution of enterprise energy structure toward low-carbon. Consequently, we propose that: H2:Climate policy uncertainty inhibits companies’ willingness to transition to low-carbon energy structures by reducing management’s long-term behavior. Green technology innovation, as the core driving force behind sustainable economic development, essentially involves enterprises achieving synergistic optimization of environmental and economic benefits through technological and institutional reforms. Based on Porter’s innovation compensation mechanism [ 30], reasonable environmental regulations are capable of prompting enterprises to convert environmental governance costs into innovation incentives, break through the path dependence of traditional development models through technological upgrades, and achieve a “win-win” situation. From the perspective of technological R&D decision-making, frequent changes in climate policies and their ambiguous objectives have led to severe information asymmetry challenges for enterprises. According to information economics theory, clear policy directions can provide enterprises with stable innovation expectations, enabling them to accurately identify market demands and technological trends [ 31] and efficiently allocate limited resources to green technology R&D. When climate policy uncertainty significantly increases, companies struggle to predict future policy standards, subsidy mechanisms, and regulatory priorities, making it difficult to determine which green technologies align with future policy requirements and market demands, ultimately weakening the efficiency of green technology innovation outcomes. In terms of innovation strategy selection, climate policy uncertainty triggers significant changes in companies’ risk perception and decision-making behavior. Faced with high climate policy uncertainty, companies may worry that the potential benefits of green investment projects and future policy incentives may not meet expectations or cover green investment costs [ 32]. Based on risk aversion principles, they may shift their innovation strategies toward low-risk incremental innovation with equivalent option values, thereby avoiding the increased costs and unstable returns associated with green technology innovation investments. Corporate energy structure decarbonization is hampered by insufficient green technology innovation skills. The decarbonization transition of energy structures fundamentally relies on the industrial application of green technology innovation outcomes. Businesses may encounter significant technical roadblocks on their way to the decarbonization transition if climate policy uncertainty results in inadequate innovation investment and postponed technology R&D. Consequently, we propose that: H3:Climate policy uncertainty inhibits the low-carbon transition of enterprise energy structure by lowering the degree of green technology innovation of enterprises. Effective investment is crucial for enterprise development, as reasonable investment can optimize resource allocation and accelerate industrial restructuring and upgrading. Based on the theory of real options, when the objectives, implementation intensity, and adjustment frequency of climate policies are highly uncertain, the future returns, cost structure, and policy support intensity of low-carbon transition projects are difficult to predict, resulting in a significant growth in enterprise investment’s waiting value. At the same time, to avoid sunk costs from irreversible investments, companies often tend to delay substantial investments in low-carbon transition projects, extending the policy observation period to avoid potential investment risks. This directly leads to delayed and inefficient investment decisions. Further analysis reveals that management’s decision-making logic and methods also exacerbate distortions in corporate effective investment structures. The principal–agent theory suggests that management faces dual constraints from term-based performance evaluations and risk preferences. The strong correlation between labor compensation and financial metrics leads them to avoid high-risk energy structure decarbonization projects, instead opting for reversible, short-term investments to achieve the goals of shortening the return cycle and reducing investment risks. This not only harms shareholder interests but also severely weakens the effectiveness and sustainability of investment decisions. In an environment of frequent changes in climate policies, the structural imbalance between compliance-driven investments and transformative investments has become increasingly prominent [ 33]. Compliance investments typically focus on equipment upgrades or process optimizations to meet current environmental standards, which can quickly achieve policy compliance objectives but are unlikely to bring about fundamental changes to a company’s energy structure. In contrast, transformative investments target areas such as low-carbon technology R&D and new energy utilization, serving as a key driver for advancing the low-carbon shift in a company’s energy structure. However, on account of the high risks associated with policy uncertainty, such investments are approached with caution by companies. Driven by policy risk avoidance motives and regulatory compliance objectives, enterprises prioritize compliance investments to meet current policy constraints. This systemic bias in investment decision-making leads to the failure of enterprise resource allocation mechanisms, resulting in insufficient resource allocation for core areas of low-carbon transition. The resulting resource misallocation effect [ 34] hinders green industrial upgrading by preventing enterprises from adopting a low-carbon, green development model. Consequently, we propose that: H4:Climate policy uncertainty inhibits the low-carbon transition of enterprise energy structure by weakening the effective investment of enterprises. Figure 1 illustrates the mechanism by which corporate energy structures’ low-carbon transition is impacted by climate policy uncertainty. 3. Methods and Data 3.1. Sample Selection and Data Sources Considering the changes in accounting norms in 2006, we use 2007–2022 Chinese A-share listed corporation data. (1) Remove ST, ST*, and PT; (2) remove financial industry; (3) remove severe data missingness in key variables and financial indicators such as debt-to-equity ratio that are significantly abnormal. Tail trimming continuous variables at the 1% and 99% percentiles reduces outliers. Ultimately, 33,205 observations from 3569 companies were obtained. China’s climate policy uncertainty index data originates from the climate policy uncertainty index constructed by Ma et al. [ 35]. The CSMAR database and Wind database are where we get the data for Huazheng ESG ratings, firm financial indicators, and governance variables. The China Urban Yearbook is where we get regional-level control variables. Data for the low-carbon energy structure index and corporate energy consumption are sourced from corporate annual reports and social responsibility reports. 3.2. Variable Definition 3.2.1. Dependent Variable We created a weighted multi-dimensional space vector included angle comprehensive index. This index divides the energy consumption structure of enterprises into coal, diesel and gasoline, and other energy sources based on the carbon emissions per unit energy. First, based on the three types of energy, three basic spatial vectors e 1 = ( 1 , 0 , 0 ) , e 2 = ( 0 , 1 , 0 ) , and e 3 = ( 0 , 0 , 1 ) are designed to serve as the basic vectors for the three types of energy. Then, design the space vector of the energy consumption structure for the i enterprise in the t year. E i , t = e 1 , i , t , e 2 , i , t , e 3 , i , t (1) Next, calculate the angles θ 1 , θ 2 , θ 3 , between E i , t and each basis vector in the previously constructed spatial coordinates, and calculate the corresponding cosine values. cos θ i , t j = e j , i , t e 1 , i , t 2 + e 2 , i , t 2 + e 3 , i , t 2 , j = 1 , 2 , 3 (2) Finally, the angles of various energy consumption for the i enterprise in the t year are weighted to obtain the energy structure low-carbon transition index (ELCI). E L C I i , t = ∑ j = 1 3 arccos θ j i , t i (3) 3.2.2. Independent Variable Drawing lessons from Ma [ 35] et al.’s research, we use manual auditing and the deep learning algorithm, the MacBERT model. Based on more than 1.75 million articles from six mainstream newspapers, including People’s Daily, Guangming Daily, Economic Daily, Global Times, Science and Technology Daily, and China News Service, a CCPU index was constructed at the provincial level in China. Given that the data is a monthly time series, this paper calculates the monthly average. Given that the provincial index has limited descriptive power for individual enterprises within a province, and that, in practice, even enterprises of different types with similar registered locations may face different climate policy uncertainty shocks, the environmental score of an enterprise reflects its environmental governance capabilities. To describe the individual characteristics of enterprises and improve model accuracy, this study multiplies the enterprise environmental score by the provincial climate policy uncertainty index, as referenced in Autor [ 36], to obtain the climate policy shock experienced by each enterprise. First, the provincial climate policy uncertainty index is normalized using the z-score method to eliminate unit differences. Next, the environmental scores of enterprises published by Huazheng are taken as negative values and normalized, where a higher environmental score indicates a smaller policy impact, and vice versa. Finally, the climate policy uncertainty shock (CCPU) that each business faces is obtained by multiplying the processed province climate policy uncertainty index by the enterprise environmental score. 3.2.3. Control Variables We select firm size (Size), firm age ( FirmAge), financial leverage ( Lev), return on assets ( ROA), return on assets ( ROE), cash ratio ( Cashflow), sales growth rate ( Growth), market value ( TobinQ), duality (Dual), the greatest shareholder’s ownership ratio ( Cap1), proportion of independent directors ( Indep), institutional investor shareholding ratio ( INST), and whether it is audited by the Big Four as control variables. Table 1 shows the particular variable definitions. 3.3. Model Design 3.3.1. Benchmark Regression Model We construct the following benchmark testing model: E L C I i , t = α 0 + β 1 C C P U i , t + β 2 X i , t + μ i + γ t + ϵ i , t (4) Among them, E L C I i , t is the explanatory variable, indicating the low-carbon transition index of the energy structure of enterprise i in year t. C C P U i , t is the core explanatory variable, representing the climate policy uncertainty shock experienced by company i in year t. X i , t indicates control variables, μ i , γ t represents firm and year fixed effects, ϵ i , t is a random disturbance term. 3.3.2. Mechanism Regression Model This paper draws on Chen [ 37] to construct the following mechanism testing model: M a c h a n i s m i t = α 0 + β 1 C C P U j , t + β 2 X i , t + μ i + γ t + ϵ i , t (5) Among them, M a c h a n i s m i t denotes a series of mechanism variables, μ i , γ t denotes firm and year fixed effects, ϵ i , t is a random disturbance term. Determine the validity of the impact mechanism based on the sign and significance of the regression coefficient β 1 . 3.4. Descriptive Statistics Table 2 shows statistical information for all variables. The standard deviation in the enterprise energy low-carbon transition index is 0.0359, the maximum value is 5.8112, and the minimum value is 5.6504, showing that this paper’s data is less affected by extreme values. Climate policy uncertainty’s standard deviation is 0.5097, with a maximum of 1.4467 and a minimum of −1.8815. This suggests that enterprise climate policy uncertainty indexes differ significantly. All other control variable distributions are fair. 4. Empirical Analysis 4.1. Baseline Results To accurately assess the influence of climate policy uncertainty on low-carbon enterprise energy structure transformation, we adopt model (1) for the benchmark regression test. The coefficient of CCPU is negatively significant in Table 3 column (1) without control variables. In Table 3, columns (2) to (4), the company’s financial variables, governance variables, and individual fixed effects are added sequentially, and the CCPU coefficient remains negative and significant. This confirms hypothesis 1. 4.2. Endogeneity 4.2.1. Instrumental Variable 4.2.2. Sample Self-Selection Problem In this paper, the samples with climate policy uncertainty greater than the median are set as the treatment group, and vice versa. Further, 1:1 nearest neighbor matching and radius matching were selected to reduce the characteristic difference. To lessen the PSM method’s sample loss and subjective setting influence, this paper adopts the entropy balance method proposed by Hainmueller [ 39] to make the distribution of higher-order moments of control variables between the treatment group and the control group close. There is no significant difference in the third moment distribution of covariates after equilibrium. The coefficients are still significantly favorable in Table 5. 4.2.3. Placebo Test We extract all the numerical values of climate policy uncertainty, and randomly assign them to each observed value for 500 regressions. If there is a placebo effect, the uncertainty of climate policy after treatment should be negatively correlated with the energy structure low-carbon transition due to the influence of unconsidered factors. Figure 2’s placebo coefficient estimates are evenly distributed on both sides of 0, ruling out placebo effect. 4.3. Robustness Test By substituting variables, performing subsample analysis, adding fixed effects, and controlling for omitted variables, we performed robustness tests. Table 6 illustrates the findings: 4.5. Heterogeneity Analysis 4.5.1. Nature of Enterprise Property Rights Considering the impact of differences in corporate ownership structure on corporate energy structure—on the one hand, this is reflected in the accuracy of policy expectations, and, on the other hand, they may more easily get financial assistance from the government or financial institutions. We divide the sample into state-owned enterprises (SOE) and non-state-owned enterprises (NSE) (see Table 8 columns (1) and (2)). Compared with SOE, climate policy uncertainty has a significant inhibitory effect on NSE at the 5% level. Climate policy uncertainty hinders NSE’s low-carbon energy structure transition, but not SOE’s. The fundamental reason lies in the institutional differences that shape different risk–benefit trade-off mechanisms. As typical market-driven entities, NSE are highly sensitive to policy uncertainties that increase investment risks and policy costs under hard budget constraints, strong market competition pressure, financing constraints, and information disadvantages, leading them to postpone or avoid high-risk low-carbon transition investments. In contrast, for state-owned enterprises, under the combined influence of soft budget constraints, multiple goals (especially political task orientation), resource advantages, relatively weakened market competition, and regulatory pressure, their low-carbon transition behaviors are more driven by national strategies and have lower sensitivity to short-term policy fluctuations. Even in the presence of uncertainties, their resource endowments and institutional guarantees provide them with stronger risk resistance capabilities and the ability and motivation to continue with the transformation tasks. 4.5.2. Environmental Regulation Intensity The Porter Hypothesis posits that when the compensatory effects of environmental planning exceed the costs of internalizing environmental considerations within a company, it promotes green development. To examine the behavioral strategies chosen by businesses under different levels of environmental regulation, this paper uses Python software 3.12 to process environmental protection-related keywords in government work reports to construct an environmental index, dividing the sample into two categories—strong environmental regulation and weak environmental regulation—according to the median value. The findings are reported in Table 8 columns (3) and (4). Climate policy uncertainty has a significant inhibitory effect on weak environmental regulation. Under weak environmental regulation, businesses are more severely hampered by the uncertainties around climate policy. Where environmental regulations are lax, considering the overall cost–benefit ratio, companies often find it more in line with shareholder interests to use funds to expand their market share. In addition, relatively lenient environmental policies and insufficient incentives to exercise green investment real options are also decisive factors. 4.5.3. Corporate Climate Risk Awareness When facing climate risks, companies should adjust their business strategies based on their own characteristics to reduce carbon emissions. Referring to Li et al. [ 41], we constructed 98 climate risk keywords for corporations and used machine learning to analyze the annual reports of listed companies to build an index of enterprise climate risk attention. Based on the median value, companies are divided into two groups. Table 8 columns (5) and (6) reveal that climate policy uncertainty has a significant inhibitory effect on low climate risk. Enterprises with low climate risk perception are more strongly impacted by climate policy uncertainty in their transition to a low-carbon energy structure. Due to their proactive climate risk mitigation initiatives, enterprises with high climate risk awareness can command better capital market valuations. High climate risk awareness companies may enhance capital expenditures, green expenditures, and employment stability to avoid market valuation discounts and job losses. 4.5.4. Policy Sensitivity Policy sensitivity is essentially the degree of correlation between the decision-making system and the policy environment, which is reflected in the ability of enterprises to identify policy signals, interpret policy intentions, and make forward-looking strategic adjustments. This sensitivity directly determines the depth and speed at which enterprises’ strategic resource allocation and long-term investment decisions are affected by the policy environment when facing climate policy fluctuations. In this paper, based on the geographical distance between the registered place of listed companies and the prefecture-level municipal government, the policy sensitivity index of enterprises is constructed, and the samples are grouped according to the annual median. Results, as shown in Table 8 columns (7) and (8), the inhibitory effect of climate policy uncertainty on the energy structure low-carbon transition is significantly stronger in the enterprise groups with high policy sensitivity, which shows that the enterprises with high policy sensitivity have significantly amplified the concerns about irreversibility and sunk cost in their investment decisions, thus delaying the optimization process of energy structure as a whole. 5. Conclusions and Insights Global climate governance’s changing policy environment affects business energy transition decisions. We take Chinese A-share listed companies from 2007 to 2022 as samples to investigate the impact of climate policy uncertainty on energy structure low-carbon transitions. Research indicates: first, the transition to a low-carbon corporate energy structure is significantly hindered by uncertainty in climate policy, the result supported by extensive robustness and endogeneity tests. Secondly, from the perspective of enterprise’s “willingness-ability”, reducing management’s long-term behavior, lowering enterprise’s green technology innovation level, and weakening the investment effect are the action mechanisms. Third, non-state-owned businesses, areas with lax environmental rules, and businesses with poor climate risk awareness are especially affected by the impact of climate policy uncertainty on the low-carbon transition of corporate energy structure. This study theoretically helps the government optimize climate policy formulation and enterprises to cope with policy uncertainty and promote low-carbon transition. The findings lead to the following policy recommendations. First, establish a climate policy framework that is “predictable, adaptable, and resilient” to address the challenge of maintaining policy stability amid the uncertainty of environmental responses. The government must focus on enhancing policy transparency and stability, avoid frequent policy changes, and provide businesses with clear and predictable policy expectations. Establish a long-term, stable climate policy framework and a phased implementation mechanism. Formulate national and provincial medium- to long-term climate strategies, clearly define the overall goals for carbon peaking and carbon neutrality, and avoid fundamental reversals in policy direction. Break down energy structure transition targets into each cycle of national economic and social development plans to maintain continuity and consistency in policy orientation. Implement a system of advance public notice and transition periods for major policy adjustments. Any climate policy adjustments that significantly affect corporate interests must be publicly announced in advance, accompanied by phased transition periods to provide enterprises with sufficient time to upgrade production equipment, implement technological upgrades, and adjust their strategies. Establish a routine policy evaluation and scientific feedback mechanism. Set up independent third-party climate policy evaluation agencies to conduct regular, comprehensive assessments of the implementation effectiveness, economic impacts, and environmental benefits of existing policies. Broadly collect feedback from enterprises, industry associations, and local governments to facilitate incremental and refined policy adjustments. Second, enhance enterprises’ risk resilience and strengthen government support for the transition. At the corporate governance level, enterprises should reform executive incentive structures by incorporating long-term metrics—such as carbon intensity reduction and green technology innovation—into performance evaluations, aligning incentives with the long-term transition timeline. Regulatory authorities should promote comprehensive reporting frameworks that emphasize long-term value metrics. The government should encourage long-term institutional investors to participate in corporate governance, establish mandatory disclosure requirements for transition strategies in key industries, and improve ESG market pricing mechanisms to ensure that companies committed to long-term sustainability gain a competitive edge. At the innovation level, enterprises should increase investment in green R&D, establish internal incentive mechanisms, cultivate and recruit specialized talent, and integrate external resources to accelerate the commercialization of low-carbon technologies. The government should increase investment in basic low-carbon research, establish national-level green technology innovation platforms, promote the formation of innovation consortia among industry, academia, and research institutions, and improve intellectual property protection systems to safeguard enterprises’ innovation returns. At the investment level, enterprises should establish climate risk assessment systems, incorporate policy risks and carbon costs into project decision-making, and optimize investment portfolios to enhance the effectiveness of green investments. The government should formulate guidelines for corporate climate risk investment assessments, provide free risk calculation and scenario analysis services through public service platforms, and establish transition risk compensation funds and policy-based guarantee systems to reduce enterprises’ investment risk exposure. Third, support differentiated policy guidance to achieve green and sustainable energy development. Increase support for the transition of non-state-owned enterprises. Establish a special fund to support the low-carbon transition of non-state-owned enterprises, and reduce their transition costs through tax breaks, equipment purchase subsidies, and additional deductions for R&D expenses. Build a platform for green technology exchange and cooperation between state-owned and non-state-owned enterprises to promote the cross-ownership diffusion of advanced low-carbon technologies and management expertise. Strengthen policy enforcement and supporting measures in regions with weak environmental regulations. In areas with relatively lax environmental regulations, intensify environmental law enforcement and supervision, raise the cost of environmental violations, and establish effective external constraints. Simultaneously, establish regional transition special funds to assist local enterprises in technological upgrades and energy structure adjustments, achieving a balance between constraints and incentives. Systematically enhance enterprises’ climate risk awareness and management capabilities. Through industry training and policy briefings, improve management’s understanding of climate risks and policy changes. Incorporate climate risk management into corporate ESG evaluation systems to guide enterprises in establishing robust internal climate risk management frameworks. Comprehensively enhance enterprises’ policy sensitivity and adaptability. Establish regular government-enterprise communication mechanisms, including periodic policy briefings and corporate roundtables, to promptly convey policy intentions. Guide enterprises in establishing policy tracking and response mechanisms to improve their ability to interpret and adapt to changes in the policy environment. Several limitations of this study warrant attention. First, this study constructs firm-level policy shock variables by cross-multiplying the provincial climate policy uncertainty index with firm environmental scores. While this method represents an improvement over macro-level indicators, it still fails to directly capture firms’ subjective perceptions of policy uncertainty. An important direction for future methodological breakthroughs is to utilize machine learning methods to extract firm-specific policy signals from annual reports, earnings call transcripts, or regulatory inquiry letters, thereby constructing a climate policy uncertainty index that truly reflects the individual firm level. Second, the indicators of low-carbon transition in corporate energy structures used in this study are calculated based on energy consumption data voluntarily disclosed by firms. Due to limitations in current disclosure standards, there are variations among firms in the classification criteria, data quality, and willingness to disclose energy consumption information, resulting in certain limitations regarding the accuracy and comparability of self-reported data. Future research could explore integrating satellite carbon emissions monitoring data, third-party verified emissions records, or on-site energy audit data to construct more objective and verifiable indicators of the low-carbon transition in corporate energy structures. Author Contributions Y.L.: Writing—Original draft, Validation, Conceptualization, Funding acquisition. Y.Z.: Writing—Original draft, Validation. H.L.: Writing—Original draft, Data curation, Conceptualization, Visualization. S.L.: Supervision, Visualization, Validation. Y.X.: Conceptualization, Supervision, Validation. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by National Natural Science Foundation of China, Grant Number: 72403109; Humanities and Social Science Fund of Ministry of Education of China (Research on the Mechanism and Path of Chain Owners’ Digitalization to Enhance the Independent Controllability of Industrial Chain), Grant Number: 24YJC790122; Shandong Provincial Natural Science Foundation, Grant Number: ZR2024QG137. Data Availability Statement The data presented in this study are available upon request from the corresponding author due to the non-free database. Conflicts of Interest Author Yanxiang Xie was employed by the company Tianjin Rural Commercial Bank. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Warsame, A.A.; Sheik-Ali, I.A.; Jama, O.M.; Hassan, A.A.; Barre, G.M. 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