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The Conference of the Parties (COP26 and 27) placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality. However, studies on the implementation of this policy proposition are limited. To address this gap in the literature, this study employs machine learning techniques, specifically natural language processing (NLP), to examine 77 climate bond (CB) policies from 32 countries within the context of climate financing. The findings indicate that “sustainability” and “carbon emissions control” are the most outlined policy objectives in these CB policies. Additionally, the study highlights that most CB funds are invested toward energy projects (i.e., renewable, clean, and efficient initiatives). However, there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019. This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry, potentially leading to the greenwashing of climate funds. Furthermore, policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change, which may negatively influence climate actions. Thus, the findings highlight that the effective implementation of CB policies depends on policy goals, objectives, and sentiments. Finally, this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

Tariff reforms in electricity markets is becoming an area of active evaluation as efforts intensify to decarbonize electricity generation, stabilize the power grid, and maximize the welfare of electricity producers and consumers. Peaks in load profiles occur when demand is not regulated, requiring huge capacity additions, which are not utilized during off-peak periods thus undermining system efficiency. With dynamic pricing of electricity, opportunities can arise to shift demand from peak to off-peak periods and this can help to reduce the need for huge capacity additions. This paper conducts a systematic review of dynamic electricity pricing literature in order to understand the evolution of research in this growing field. 218 relevant journal papers published between 1949 and 2020 are identified from which thematic maps and ontological tables are drawn to understand key subject areas that have emerged over the years and their interlinkages. Six major thematic areas of dynamic electricity pricing research are reported including 1) pricing scheme and modeling, 2) pricing impacts, 3) user demand response, 4) consumption scheduling, 5) load scheduling technologies, and 6) cybersecurity threats and fairness issues. These six major thematic areas are then broken down into twenty second-order themes and fifty eight first-order themes. The review provides an overview of the development of the field while providing insights on its future development. It concludes that, despite coherent themes, the research on dynamic electricity pricing has enormous potential for follow-up work and theory development.

Some research on climate change has been the basis of climate change denialism (hereafter, CCD is used to refer to denial, denialism, and deniers). There is formative knowledge about the role of political propaganda in climate policies and resulting outcomes. To contribute to the understanding of political ideology and the extent of CCD, we adopt econometric techniques to study the impact of the United States of America's withdrawal from the Paris Agreement. The results show that ideological political propaganda has psychological and philosophical consequences that impact climate change policies. In addition, we find that the effect of a state's economic growth on climate change is mediated by CCD. In particular, low-income capitalist states have a higher propensity to become CCDs because they are more likely to engage in economic expansion even at the expense of environmental sustainability. Global climate change policies depend on high-income nations and industries' willingness to adopt economic policies to achieve sustainable future development. Thus, this study fills the literature gap on the relationship between political ideology and climate change. The findings show that CCD significantly influences voting patterns and socioeconomic outcomes. It impedes states from achieving net-zero emissions and carbon neutrality and it is used as a political propaganda. Subject to these findings, relevant policy suggestions are offered.

This paper presents a time-of-use (TOU) pricing model of the electricity market that can capture the interaction between power plants, generation ramping, storage devices, electric vehicle loading, and electricity prices. Even though the model is calibrated to Chinese Fujian electricity market, it can be used to predict efficient equilibrium tariffs on any electricity system that seeks to emphasize the marginal costs principle for generators where consumer tariffs are regulated. We show that, for efficiency purposes, it is imperative for dynamic electricity pricing models to take into account technology-specific ramping parameters and various sources of power system flexibility. Also, by utilizing unused battery capacity, electric vehicles can significantly complement other storage devices to reduce power sector emissions and ensure grid stability. The model and results presented in this paper are useful to regulatory bodies in Fujian province for economic policy, and to the scientific community in terms of (i) offering a tool to promote more discussion on dynamic pricing in electricity markets, and (ii) enriching our understanding of how to design a TOU program in the light of flexible power resources.

Carbon emission is inevitable, and changes with energy consumption and economic development, presents policy options toward sustainable development path. Currently, there is little assurance from policymakers in committing to climate change pledges: taking the Middle-East and North African (MENA) region as a specific case with using 2019 as a cut-off period. We conducted an interim assessment of the Paris Agreement to ascertain whether climate actions are in tandem with emissions reduction targets. Making use of difference-in-difference technique as a quasi-experiment supported by fixed-effects and placebo treatment models, the results point to evidence of less than 1% effective CO2 emissions reduction as of 2019 compared to the 2015 level. The current carbon emissions reduction commitment level is far-reaching contrary to the Nationally Determined Contributions (NDCs) pledged (16.43% on average by 2030 compared to the 2015 level). The analysis suggests that achieving NDCs commitments are currently major burden on policymakers since the economic development is highly linked with nonrenewable energy consumption. Furthermore, a more comprehensive framework when accounting for all available renewable and clean energy projects shows reduction levels in the range of 30%–40% from 2020 to 2030. These results suggest that the Paris Agreement in MENA countries may be more effective from 2020, thus underscore the importance of capturing ongoing projects (involving renewable and clean energy technologies) into interim policy assessment. The shared implication is that greater efforts are demanded from the region and at country levels to further decrease emissions through the use of negative emissions technologies particularly in the electricity generation sector.