Carbon Pricing and Renewable Deployment in Saudi Arabia: Policy Strategies for Growth and Decarbonization

Investigating equity and efficiency in carbon pricing with revenue recycling: A combined macro- and micro-modelling approach

Heterogeneous responses to carbon pricing: Firm-level evidence from Beijing emissions trading scheme

A hybrid model for carbon prices: Integrating investor attention, mixed-frequency data, quantile regression and deep learning

This paper presents a refined country-level integrated assessment model, Framework for Uncertainty, Negotiation, and Distribution (FUND) 3.9n, that extends the regional FUND 3.9 framework by incorporating sector-specific climate impact functions and parametric uncertainty analysis for 198 individual countries. The model enables estimation of the national social cost of carbon (NSCC), capturing heterogeneity across nations from economic structure, climate sensitivity, and population exposure. Our results demonstrate that both the NSCC and the global sum estimates are highly sensitive to damage specifications and preference parameters, including the pure rate of time preference and relative risk aversion. Compared to aggregated single-sector approaches, the disaggregated model with uncertainty yields higher values of the NSCC for low- and middle-income countries. The paper contributes to the literature by quantifying how sector-specific vulnerabilities and stochastic variability amplify climate damages and reshape global equity in the distribution of the NSCC. The NSCCs derived from our model offer policy-relevant metrics for adaptation planning, mitigation target setting, and equitable burden-sharing in international climate negotiations. This approach bridges the gap between globally harmonized carbon pricing and nationally differentiated climate impacts, providing a theoretically grounded and empirically rich framework for future climate policy design.

<p>This paper develops an integrated framework to value China Certified Emission Reductions (CCER) in the context of the national emissions trading system. At the micro level, we refine the income approach by endogenizing firms' CCER purchase decisions under emission uncertainty, offset caps and residual value risk, deriving a closed-form marginal willingness-to-pay schedule linked to firm-specific emission distributions, allowance allocations and policy parameters. At the macro level, we model carbon prices with a three-regime switching geometric Brownian motion calibrated to Beijing carbon market and electricity data, and price CCER as a real-option-like asset with state-dependent CEA-CCER spreads and guarantee-type payoffs. Comparing the two layers, we show how income-based benchmarks and regime-switching option values differ yet can be aligned to inform CCER pricing, contract design and policy reform in China's carbon market.</p>

This study comparatively analyzes the effects of carbon pricing and incentive policies on the economic feasibility, quantified using the levelized cost of hydrogen (LCOH) of green hydrogen investments. A MATLAB-based simulation model was developed using a dataset based on 2025 projections for six countries/regions (the US, the EU, China, Japan/South Korea, Russia, and Türkiye). Model outputs show that carbon pricing alone is insufficient to increase the competitiveness of green hydrogen; however, when combined with production and investment incentives, it leads to a significant reduction in LCOH. The production incentive of up to USD 3/kg provided under the U.S. Inflation Reduction Act (IRA) stands out as the most effective policy tool for accelerating investments by reducing LCOH to USD 2/kg. In contrast, the EU’s high carbon pricing mechanism increases gray hydrogen costs but does not directly reduce costs. Scenarios conducted for Türkiye indicate that a combination of a USD 1/kg production incentive and a USD 25–30/tCO2 carbon price could reduce LCOH from USD 5–6/kg to USD 2.5–3/kg. The study emphasizes that the future of the green hydrogen economy will be shaped not only by technological progress but also by predictable and integrated policy design.

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets.

Background: The rapid shift toward renewable energy and net-zero climate policies has raised concerns about greenflation—temporary upward pressure on consumer prices arising from the green energy transition itself. While the topic has attracted increasing attention from policymakers and central banks, a systematic synthesis of recent empirical evidence remains limited. Aim: This systematic review evaluates the existence, direction, and persistence of short-run inflationary effects associated with key elements of the green transition, including carbon pricing policies, energy-market adjustments, and large-scale green investment programmes. Methods: Following PRISMA-guided reporting principles, we searched Scopus, Web of Science, EconLit, RePEc/IDEAS, SSRN, Google Scholar, and major central-bank and international-organisation repositories for studies published between 2015 and 2025. Eligible studies provided empirical or model-based estimates of the impact of real-world green-transition policies or shocks on headline or core consumer price inflation within the first five years. After screening 1,322 records, eight recent high-quality empirical studies were included and assessed using a modified Newcastle–Ottawa framework. Findings were synthesised using structured narrative and comparative quantitative approaches. Results: Across different regions, methods, and policy settings, the included studies consistently report positive but temporary inflationary effects associated with the green energy transition in the short run. Carbon pricing mechanisms and energy-related transition shocks emerge as the most prominent contributors, while green investment-driven demand effects are generally smaller. The magnitude of reported effects varies across studies and contexts but is commonly described as modest and transitory, with inflationary pressures fading as energy systems adjust and supply constraints ease. Conclusion: The available empirical evidence suggests that greenflation exists but is limited in scale and duration. Short-run inflationary pressures linked to the green transition are unlikely to pose a major obstacle to price stability if appropriately managed. Well-designed climate policies, particularly those combining carbon pricing with revenue recycling and supply-side measures can further mitigate short-term price impacts while preserving long-term climate objectives.

In this study, a rolling-window estimation test is used to examine the dynamic causalities between China's carbon emission price (CEP) and the new energy industry (NEI). According to the results, the CEP affects the NEI in both positive and negative ways over various subperiods. The positive effect of the CEP on the NEI stems from economic slowdowns during external shocks (e.g., the COVID-19 pandemic), whereas funds released from a lower CEP have a negative effect, enabling enterprises to develop new energy. In contrast, the NEI negatively affects the CEP through two channels. A higher cost triggers a decline in the NEI, driving up the CEP, whereas recovery of the NEI suppresses the CEP by reducing fossil fuel demand and emissions. The findings reveal dynamic causalities, enriching the theoretical framework of carbon trading market–new energy industry interactions and offering valuable insights into China's “dual carbon” goals and energy transition strategies. Thus, the following recommendations are proposed for three stakeholders: governments should improve carbon market efficiency, enterprises should transition to low-carbon operations, and research institutes should develop new technologies.

Carbon trading, as the critical market mechanism to achieve greenhouse gas emotion reduction, have received more attention around the world. The target that China establish and improve Carbon Emissions Trading Exchange (CCET) in recent year is to help the achievement of “peak carbon dioxide emissions”, “carbon neutrality”. However, Considering the characteristics of nonlinearness, strong fluctuation of carbon price, the accuracy of data prediction reduced significantly. Based on China Carbon Emissions Trading Exchange (CCET) data from July 2021 to March 2025, this paper build a multi-factor hybrid forecasting model that integrates VMD-CEEMDAN decomposition with GARCH-MIDAS and CNN-BiLSTM-Attention model throughout the ARCH and Sample Entropy as testing methods. Meanwhile, this model introduce influence factor including Macro Economy, Similar Products, Energy Structure and Climate Environment. Combine the random forest method with the MIDAS approach to select and integrate multi-frequency influencing factors, and evaluate model performance using indicators such as MAE, MSE, and RMSE. The experimental results show that the proposed model is better than the other model in accuracy and stability. The indicators including MAE, MSE and RMSE all are less than other comparison models.

With the acceleration of urbanization and the continuous rise in private car ownership, urban traffic congestion has become a critical issue constraining sustainable development. As an important extension of carbon reduction policies, the personal carbon trading mechanism provides a new approach to regulate travel behavior through economic incentives. This study constructs a game model incorporating stakeholders from both government and private car owners, explores their decision-making behaviors under the personal carbon trading mechanism, and conducts simulation analysis of evolutionary paths using MATLAB 2019a. The findings reveal that choosing public transportation results from interactive strategic interactions between government and private car owners. Proactive implementation of personal carbon trading policies by the government can accelerate private car owners’ adoption of public transportation strategies. Reducing government implementation costs of personal carbon trading (PCT), increasing carbon trading costs for private cars (through higher carbon prices or lower allowances), and improving public transit comfort are key factors in achieving equilibrium between government and private car owners’ strategies. Carbon trading costs exhibit differentiated impacts on the convergence speed of both parties’ states. This research aims to provide decision-making references for governments in formulating and implementing personal carbon trading systems, as well as motivating private car owners to adopt green and environmentally friendly travel behaviors.

This study leverages the implementation of China's pilot Emissions Trading Scheme (ETS) policies as a quasi-natural experiment. Utilizing panel data from 284 prefecture-level and above cities in China spanning 2003 to 2017, we employ a multi-period difference-in-differences (DID) approach to empirically investigate the carbon reduction effects, underlying mechanisms, and heterogeneity associated with the ETS pilots. The findings reveal that: (1) The ETS effectively reduces both the total carbon emissions and carbon intensity in the pilot regions through dual channels of aggregate control and efficiency improvement. This conclusion remains robust after a series of rigorous tests. (2) The ETS achieves carbon reduction in pilot cities through the combined action of market mechanisms and administrative interventions. While the role of market mechanisms is relatively limited, administrative interventions contribute significantly. (3) The carbon reduction effects of the ETS exhibit significant heterogeneity. From the perspective of urban resource endowment, non-resource-based cities outperform resource-based cities in carbon reduction effectiveness. Regarding allowance allocation methods, regions employing a hybrid approach combining free allocation with auction and fixed-price sales demonstrate superior carbon reduction outcomes compared to those using solely free allocation or a combination of free allocation and auction. In terms of carbon price, the ETS policy demonstrates greater effectiveness in reducing total carbon emissions in high-carbon-price regions than in low- and medium-carbon-price regions, but its effectiveness in reducing carbon intensity is weaker in high-price regions compared to low- and medium-price regions. This study provides empirical evidence and policy insights for China to tailor the implementation of its ETS to local conditions, thereby advancing the achievement of its "dual carbon" goals (carbon peaking and carbon neutrality) and fostering green transition development.

Given that North American cross-border logistics account for 7.3% of global carbon emissions and enterprises are confronted with the practical dilemma of “high decarbonization costs,” coupled with the existing research focusing predominantly on single transportation modes and the failure to internalize carbon costs, this study embarks on a novel approach by internalizing carbon costs. It constructs a carbon footprint-sensitive multimodal transportation path algorithm. Leveraging 1.5 million freight waybills data from five core cross-border corridors in North America (two U.S.-Canada and three U.S.-Mexico) between 2021 and 2023, this study calculates the unit carbon emission coefficients for dry goods, cold chain, and hazardous materials under maritime, rail, and road transportation modes. A cost-carbon balanced optimization model is established with dual objectives of minimizing comprehensive costs and carbon emissions. The empirical case analysis of electronic components transportation across the U.S.-Mexico border at El Paso-Juarez reveals that the optimized path combining maritime and rail transportation, compared with the traditional road-dominated path, can achieve a 40.6% reduction in carbon emissions (monthly carbon emissions decreased from 1,280tCO₂ to 760tCO₂), with a net cost increase of only 3.2%. When the carbon price rises to USD 100 per ton of CO₂, the model automatically increases the proportion of rail transportation to 65%, achieving a decarbonization rate of 58%. The study confirms that internalizing carbon costs can effectively balance the economic efficiency and environmental decarbonization goals of North American cross-border logistics. The substitution of some road transportation by maritime and rail transportation is identified as the core path for regional logistics decarbonization. The large-scale promotion of this path across the top ten cross-border corridors in North America has an annual decarbonization potential of 1.42 million tons of CO₂.

Precise forecasting of fluctuations in carbon allowance valuations is critical for shaping environmental policy and for bolstering the effectiveness of market-based regulatory mechanisms. Advanced statistical and machine-learning techniques afford regulators the capacity to fine-tune carbon taxation schemes, enhance the operational efficiency of emissions trading frameworks, and steer financial resources toward low-carbon development projects with greater assurance. This study examines the China Emissions Trading Scheme (CHNTS)-one of China's pioneering carbon markets established under the broader national decarbonization strategy-and presents an innovative predictive model based on Gaussian process regression (GPR) whose hyperparameters are optimized through a Bayesian framework. By dynamically adjusting to latent market behaviors and unobserved structural shifts, this method adapts more responsively to evolving trading patterns. Our empirical investigation utilizes daily settlement data for China Emission Allowances spanning July 16, 2021, through April 9, 2025-a timeframe marked by key regulatory amendments, market maturation phases, and changing participant conduct as the scheme integrated into the wider national carbon pricing system. Model validation is performed on an out-of-sample window from June 28, 2024, to April 9, 2025, yielding notable performance metrics: a relative root-mean-square error (RRMSE) of 1.0771%, root-mean-square error (RMSE) of 1.0212, mean absolute error (MAE) of 0.6773, and a correlation coefficient (CC) reaching 98.604%. To the best of our knowledge, this represents the first deployment of GPR in the context of China's carbon trading exchanges. Beyond enriching theoretical understanding of price discovery in emergent emissions markets, the proposed approach provides a flexible analytical template that could readily be applied to analogous cap-and-trade systems worldwide.

Abstract This paper examines how market-based policy interventions can enhance the economic viability of e-fuels for aviation and shipping, two hard-to-decarbonise sectors. A novel optimisation-based market penetration assessment model is developed and applied to three case studies, one based on a global shipping scenario with e-Ammonia and e-Methanol, and two based on national scenarios: UK aviation with advanced SAF and e-SAF. The optimisation model quantifies the effectiveness of Contracts-for-Difference (CfD), Carbon Contracts-for-Difference (CCfD), and optimised policy mixes in reducing e-fuel costs. Findings reveal that a combined policy approach can close the price gap between e-fuels and conventional fossil fuels. For instance, a carbon tax of $33.6–$162.8 per tonne CO 2 , paired with subsidies of $32.3–$79.15 per MWh, could lower e-Ammonia costs by 24%, and e-Methanol by 29% in shipping. In the UK aviation sector, CfD schemes designed to stabilise advanced SAF prices have the potential to reduce government support costs by up to 30%, making advanced SAF more competitive. Moreover, implementing CCfDs linked to emissions reductions for e-SAF could achieve cost parity by 2040 when combined with carbon pricing projected at $150 per tonne CO 2 . This research supports a more informed and data-driven policy design, ensuring that economic barriers to synthetic fuel adoption are addressed through a balanced, market-driven approach. Graphical Abstract

RF-LSTM carbon price prediction based on CEEMDAN decomposition and multiscale entropy reconstruction.

This study aims to discover the private sector’s perspective on the sustainable transition of degraded forestlands, including the transformation of oil palm plantations in forest areas into multipurpose forests, identify current barriers in adopting sustainable multipurpose forest management practices on financing and policy aspects, and explore how the private sector can step up its role in forestland restoration. This study was based on field observations, key informant interviews, focus group discussions (FGDs), and literature reviews. This study aims to navigate a path for policy implementation toward decarbonization, as tenurial conflicts, particularly between oil palm plantations and forest areas, are critical for sustainable forest management in Riau. The private sector's interest in sustainable multipurpose forest management is higher when additional benefits from non-timber forest products (NTFPs) are high. This study also found that the private sector’s desire to support sustainable multipurpose forest management stems from the potential benefits of carbon trading. Regarding the carbon market, most respondents are willing to join when carbon prices are USD4–6 ton-1 of CO2e. It indicates that the private sector is willing to support the domestic carbon market as regulated under the Minister of Environment and Forestry Regulation 21/2022. While the private sector has complied with most transformative policies and mechanisms, respondents expect further incentives and support, particularly to resolve the forestland conflict.

The risks associated with carbon credit transactions discourage private investment in Reducing Emissions from Deforestation and forest Degradation in tropical countries (REDD+) and reduce incentives for forest conservation in tropical countries. REDD+ call options have been proposed as a carbon trading mechanism that reduces uncertainty and attracts private investment. Meanwhile, assessing their rationale for tropical nations remains limited. This study evaluated the payoffs of REDD+ call option contracts from the perspective of tropical countries. Using the Black-Scholes model, we identified strike prices and option prices that offset the costs of REDD+. Then we simulated carbon prices with geometric Brownian motion to examine option payoffs. The revealed payoff suggests that tropical countries are prone to losses under the REDD+ call option, although it represents a rational investment strategy for corporations. A short straddle-like payoff diagram suggests limited benefits under high market volatility, while even low volatility also fails to make the mechanism attractive due to insufficient upfront payments. Robustness checks using a Merton jump–diffusion process also confirmed that these qualitative payoff patterns persist under more realistic carbon price dynamics. To increase the practical applicability, we proposed alternative strategies such as covered call options. The study concludes that REDD+ call options should be adopted cautiously and complemented by appropriate risk management measures.

ABSTRACT I show that carbon pricing can hurt green technology innovation due to firm financial con- straints. I identify green technologies from patent filings through textual analysis of patent claims and construct a novel dataset that links firm green patenting to ownership of carbon- emitting plants. Focusing on firms with high emissions in California, I show that the California cap-and-trade program exacerbates financial difficulties in already constrained firms, forcing them to reduce R&D and, subsequently, green innovation. These effects offset the positive im- pact of cap-and-trade on green innovation in unconstrained firms, rendering the program less effective in stimulating much-needed green innovation. The results suggest that carbon pricing forces constrained firms to value their short-term cash flows more than the long-term cash flows from transitioning to green technology.