Potential for Return on Investment in Rehabilitation-Oriented Blue Carbon Projects: Accounting Methodologies and Project Strategies

Opportunities to boost climate change mitigation and adaptation (CCMA) and sustainable conservation financing may lie in enhancing blue carbon sequestration, particularly in developing nations where coastal ecosystems are extensive and international carbon markets offer comparatively attractive payments for environmental stewardship. While blue carbon is receiving increased global attention, few credit-generating projects are operational, due to low credit-buyer incentives with uncertainty in creditable emissions reductions and high project costs. Little empirical guidance exists for practitioners to quantify return-on-investment (ROI) and viability of potential projects, particularly for rehabilitation where multiple implementation options exist with diverse associated costs. We map and model drivers of mangrove natural regeneration (NR) using remote sensing (high-resolution satellite imagery segmentation and time-series modeling), and subsequent carbon sequestration using field- and literature-derived data, across abandoned aquaculture ponds in the Philippines. Using project-specific cost data, we then assess ROI for a hypothetical rehabilitation-focused mangrove blue carbon project at a 9.68 ha abandoned pond over a 10-year timeframe, under varied rehabilitation scenarios [NR vs. assisted natural regeneration (ANR) with planting], potential emissions reduction accreditation methodologies, carbon prices and discount rates. NR was faster in lower-lying ponds with lower tidal exposure (greater pond dike retention). Forecasted carbon sequestration was 3.7- to 5.2-fold and areal “greenbelt” regeneration 2.5- to 3.4-fold greater in our case study under ANR than NR. Variability in modeled sequestration rates drove high uncertainty and credit deductions in NR strategies. ROI with biomass-only accreditation was low and negative under NR and ANR, respectively. ROI was greater under ANR with inclusion of biomass and autochthonous soil carbon; however, neither strategy was highly profitable at current voluntary market carbon prices. ANR was the only scenario that fulfilled coastal protection greenbelt potential, with full mangrove cover within 10 years. Our findings highlight the benefits of ANR and soils inclusion in rehabilitation-oriented blue carbon projects, to maximize carbon sequestration and greenbelt enhancement (thus enhance pricing with potential bundled credits), and minimize forecasting uncertainty and credit-buyers’ perceived risk. An ANR rehabilitation strategy in low-lying, sea-facing abandoned ponds with low biophysical intervention costs may represent large blue carbon CCMA opportunities in regions with high aquaculture abandonment.

The effectiveness of China’s regional carbon market pilots in reducing firm emissions

China has implemented an emission trading system (ETS) to reduce its ever-increasing greenhouse gas emissions while maintaining rapid economic growth. With low carbon prices and infrequent allowance trading, whether China’s ETS is an effective approach for climate mitigation has entered the center of the policy and research debate. Utilizing China’s regional ETS pilots as a quasi-natural experiment, we provide a comprehensive assessment of the effects of ETS on firm carbon emissions and economic outcomes by means of a matched difference-in-differences (DID) approach. The empirical analysis is based on a unique panel dataset of firm tax records in the manufacturing and public utility sectors during 2009 to 2015. We show unambiguous evidence that the regional ETS pilots are effective in reducing firm emissions, leading to a 16.7% reduction in total emissions and a 9.7% reduction in emission intensity. Regulated firms achieve emission abatement through conserving energy consumption and switching to low-carbon fuels. The economic consequences of the ETS are mixed. On one hand, the ETS has a negative impact on employment and capital input; on the other hand, the ETS incentivizes regulated firms to improve productivity. In the aggregate, the ETS does not exhibit statistically significant effects on output and export. We also find that the ETS displays notable heterogeneity across pilots. Mass-based allowance allocation rules, higher carbon prices, and active allowance trading contribute to more pronounced effects in emission abatement.

Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems

In the power and heat sectors, the uncertainty of energy and carbon prices plays a decisive role in the rationale for decommissioning/repurposing coal-fired CHP (combined heat and power) systems and on investment decisions of energy storage units. Therefore, there is a growing need for advanced methods that incorporate the stochastic disturbances of energy and carbon emission prices into the optimization process of an energy system. In this context, this paper proposes an integrated method for investigating the effects of uncertain energy and carbon prices on the operational patterns and financial results of CHP systems with thermal energy storage units. The approach combines mathematical programming and Monte Carlo simulation. The computational process generates feasible solutions for profit maximization considering the technical constraints of the CHP system and the variation of energy and carbon emission prices. Four scenarios are established to compare the operational patterns and economic performance of a CHP system in 2020 and 2030. Results show that in 2020, there is an 80% probability that the system’s annual profit will be less than or equal to €30.98 M. However, at the same probability level, the annual profit in 2030 could fall below €11.88 M. Furthermore, the scenarios indicate that the incorporation of a thermal energy storage unit leads to higher expected profits (€0.74 M in 2020 and €0.71 M in 2030). This research shows that coal-fired CHP plant operators will face costly risks and potentially greater challenges in the upcoming years with the increasing regulatory and financial pressure on CO2 emissions and the EU’s plan of phasing out fossil fuels from electricity and heat generation.

The Impact of Socio-Economic Inertia and Restrictions on Net-Negative Emissions on Cost-Effective Carbon Price Pathways

Many countries have indicated to plan or consider the use of carbon pricing. Model-based scenarios are used to inform policymakers about emissions pathways and cost-effective carbon prices. Many of these scenarios are based on the Hotelling rule, assuming that a carbon price path increasing with the interest rate leads to a cost-effective strategy. We test the robustness of this rule by using experiments with plausible assumptions for learning by doing, inertia in reducing emissions, and restrictions on net-negative emissions. Analytically, we show that if mitigation technologies become cheaper if their capacities are increased, Hotelling does not always apply anymore. Moreover, the initial carbon price is heavily influenced by restrictions on net-negative emissions and the pathway by both restrictions on net-negative emissions and socio-economic inertia. This means that Hotelling pathways are not necessarily optimal: in fact, combining learning by doing and the above restrictions leads to initial carbon prices that are more than twice as high as a Hotelling pathway and thus to much earlier emission reductions. The optimal price path also increases less strongly and may even decline later in the century, leading to higher initial abatement costs but much lower long-term costs.

German carbon dividend drive will inspire more nations

Significance With the advent of China’s national emissions trading system in the past year, carbon prices now cover more than one-fifth of global greenhouse gas emissions. Yet doubts remain over whether they are high enough to force decarbonisation. Impacts Higher carbon prices will be more effective in curbing emissions than the relatively low prices that have prevailed in the past. Germany’s new coalition government will include the Greens and Free Democrats, parties committed to carbon dividends and carbon pricing. The successful rollout of this policy in Germany could serve as a model for Europe and beyond.

Mechanism Analysis of Applying Blockchain Technology to Forestry Carbon Sink Projects Based on the Differential Game Model

As an important way to reduce emission, forestry carbon sink (FCS) has not been implemented effectively. Therefore, this paper aims to analyze the effectiveness and mechanism of applying blockchain technology in FCS projects by utilizing the differential game model. A Stackelberg differential game model between forest farmers and emission-controlled enterprises (ECEs) is developed to analyze the optimal emission reduction efforts and the optimal trajectory of forest farmers and ECEs before and after introducing blockchain technology. It is found that: (1) At the initial stage of the utilization of blockchain technology, if blockchain technology takes a leading role in stabilizing carbon prices, the ECEs prefer to purchase FCS instead of reducing emissions by their own technology. On the contrary, if blockchain technology takes a leading role in stimulating the vitality of the carbon trading market, ECEs tend to use emission abatement technology to meet the carbon quote requirements. (2) In the later stage, the incentive and stabilizing effects of blockchain technology on carbon prices tend to be balanced, and the emission reduction efforts of ECEs are lower than the efforts before applying blockchain technology. (3) The application of blockchain technology increases forest farmers’ willingness to reduce emissions because of its effection of cost reduction and efficiency improvement. Meanwhile, blockchain technology reduces abatement costs by influencing carbon prices. Therefore, blockchain technology improves forest farmers’ emission reduction efforts on the whole.

An Ensemble Prediction System Based on Artificial Neural Networks and Deep Learning Methods for Deterministic and Probabilistic Carbon Price Forecasting

Carbon price prediction is important for decreasing greenhouse gas emissions and coping with climate change. At present, a variety of models are widely used to predict irregular, nonlinear, and nonstationary carbon price series. However, these models ignore the importance of feature extraction and the inherent defects of using a single model; thus, accurate and stable prediction of carbon prices by relevant industry practitioners and the government is still a huge challenge. This research proposes an ensemble prediction system (EPS) that includes improved data feature extraction technology, three prediction submodels (GBiLSTM, CNN, and ELM), and a multiobjective optimization algorithm weighting strategy. At the same time, based on the best fitting distribution of the prediction error of the EPS, the carbon price prediction interval is constructed as a way to explore its uncertainty. More specifically, EPS integrates the advantages of various submodels and provides more accurate point prediction results; the distribution function based on point prediction error is used to establish the prediction interval of carbon prices and to mine and analyze the volatility characteristics of carbon prices. Numerical simulation of the historical data available for three carbon price markets is also conducted. The experimental results show that the ensemble prediction system can provide more effective and stable carbon price forecasting information and that it can provide valuable suggestions that enterprise managers and governments can use to improve the carbon price market.