Effective Mitigation Policies Research Articles

Evaluating the Crucial Relationships between Soil Health and Climate Change

Content of paper emphasizes the significant influence of soil health on global warming and climate stability. Soil affects the Earth's carbon cycle by acting as a source and sink of greenhouse gases. Climate is considered important a factor in soil formation, but also soils also have an impact on the climate. Therefore, soils are affected by the current climate change as well, particularly because they may release excessive amounts of carbon dioxide (CO2) and other greenhouse gases (GHG) as a result of changing their usage or poor management or human wrong practices. The pressure that climate change places on soil ecosystems has a significant impact on agriculture, biodiversity, and ecosystem services. For effective climate mitigation and adaptation policies to be developed, it is essential to understand these relationships. Worthwhile that the greatest soil management practices for reducing climate change focus on increasing soil organic matter content and water retention. The enormous carbon and nitrogen (N) store that is soil. acting as a possible regulator of the CO2, N2O, and CH4—the three main greenhouse gases in the atmosphere. Because biota is the organic matter carrier to the soil, both in living organisms and through their decomposing remains, mature soils rich in life are frequently CO2 sinks. But heterotrophic organisms that live in soil are also OM net consumers, and they release CO2 into the atmosphere through respiration. The interactions between plants, soil organisms, and abiotic soil conditions are actually changing as a result of climate change, which has an impact on plant performance, plant diversity, and community structure. The loss of soil organic matter (OM) and its development to the atmosphere quickly became a significant effect of tillage and any other activity, agricultural or otherwise, that disturbs the natural soils since then. It is anticipated that the areas affected by salinization would increase due to climate change which affects the suitability of the soil agriculture or any other development purposes.The conclusion of the current study Carbon sequestration can reduced the green house gas emission.

External finance for climate change mitigation: Assessing the impact of energy aid and total aid inflows on the ecological footprint

This study examines the role of energy aid and total aid inflows in the ecological footprint of BRICS countries while controlling the economic growth, energy consumption, remittance inflows, and foreign direct investment inflows for the period spanning from 1992 to 2016. The empirical outcome of this work shows the long-run relationship for the selected variables. We find that overseas energy aid, total aid, and foreign direct investment inflows reduce ecological footprint while economic growth, remittance inflows, and energy consumption fuel it. These findings are robust across the alternative panel techniques used. However, the results imply that the energy aid, total aid, and investment capital coming from other countries and international agencies to BRICS countries are enhancing green energy plans rather than profiteering. This may be one of the reasons for the reduction of the ecological footprint in BRICS countries. Therefore, this study further suggests an effective climate mitigation policy for ensuring a life-sustaining green environment if the governments of BRICS countries can stimulate greater inflows of overseas energy aid, total aid, and foreign direct investment towards making the investment in green energy plans.

Carbon tax on milk products and the exact consumer welfare measure in emerging economies

AbstractCurrent milk prices do not account for the emissions, environmental externalities, and the excessive resource use that occur during milk production. Imposing a “carbon tax on food” can be a very effective climate change mitigation policy and the ultimate distributional effect depends heavily on the way the tax is implemented. To offset the regressive financial effect, redistribution measures need to be designed and the financial welfare loss can serve as a first orientation for designing these redistribution measures. Current literature mainly uses estimated elasticities from empirical demand systems to approximate the welfare change which can lead to a substantial bias if the tax rate is sufficiently large. In contrast, we calculate the welfare change by using the Exact Consumer Welfare of a carbon tax on milk for selected emerging economies. We show that on average, consumers in upper‐middle‐income economies would face a financial welfare loss of 19.4–176.9 USD (in the value of 2017) measured by the compensating variation (CV), depending on the tax scenario. Among a set of 16 emerging economies, consumers in Argentina (ARG) and Türkiye (TUR) face the highest financial welfare losses. We also find that discrepancies between the Exact Consumer Welfare and the consumer surplus are very small. [EconLit Citations: Q11, Q18].

The elasticity and efficiency of carbon reduction strategies in transportation

ABSTRACT Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [−2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [−354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings’ interpretation should consider the inherent data limitations.

Development of recommendations for the implementation of seismic risk mitigation policy in the Russian Federation based on world experience

The current state of practice of seismic risk mitigation policy in the following countries is analyzed: Japan, USA, China, Italy and the Russian Federation. In particular, the paper compares seismic retrofit policies for existing buildings and financing mechanisms for comprehensive seismic risk mitigation in the context of each country's regulatory framework. The analysis of the current seismic risk mitigation policy in the Russian Federation leads to the conclusion that it needs serious modernization and reorganization, including: 1) development of a new generation of standards specifying nonlinear analysis methods and 2) development of effective financing mechanisms for the seismic evaluation and seismic modernization of the existing building stock. 
 The aim of this paper is to identify best practices in seismic retrofitting policy that may be useful for the implementation and adjustment of seismic risk mitigation policies in the Russian Federation at the federal and regional levels. 
 The result of the paper is a set of recommendations based on the most effective seismic risk mitigation policy instruments, grouped according to the key stages of the seismic amplification process:
 – risk assessment of existing buildings;
 – knowledge transfer; 
 – setting targets for the seismic modernization; 
 – implementation of the seismic strengthening policy;
 – policy implementation monitoring.

Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation

Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth’s water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended.

Designing a virtuous cycle: Quality of governance, effective climate change mitigation, and just outcomes support each other

Climate change mitigation is mostly assessed through the lens of technologies and policy instruments. However, governance and social capital are crucial factors in complex social systems and may be relevant in the formation of effective climate policies. Here, we investigate the role of quality of governance (QoG), social capital, and equality as preconditions for enacting climate policies. Relying on indicators of social systems at nation state level, we investigate relationships with Qualitative Comparative Analysis (QCA) and Structural Equation Models (SEM). We find that quality of governance, measured as impartiality, underpins social capital and interpersonal trust, equality and effective climate mitigation policies, indicated by the level of carbon pricing. Impartiality and social capital are necessary conditions for climate policies. Socio-economic inequalities reduce trust and political engagement, and thus compromise the overarching goal of climate change mitigation. Evidence from complementary literature indicates that fairly implemented climate policies could foster a virtuous cycle that further improves quality of governance, and thus the capacity for implementing strong climate policies. Our results demonstrate that impartial governance and resulting social capital form the underpinnings of effective climate policies.

Who Is Responsible for the Climate Crisis? A Perspective from the Global South

ABSTRACT This article argues that insistence on adaptation to climate change, popularised by the rich nations of the Global North, is crowding out discussions on mitigation strategies. Mitigation (or degrowth) is central to formulating a fair and collective strategy to tackle the climate crisis at the global scale. In this context, we have calculated the historical cumulative emissions, material footprints, and carbon debt, which are essential prerequisites to tackling the climate crisis. This article empirically proves that the Global North owes a huge carbon debt based on historical cumulative emissions. We estimate the carbon debt of all the outlier countries (carbon debtors) to be US$ 3127.28 trillion, of which the US (the United States) and EU (European Union, including 28 countries) owe US$ 1174.19 and US$ 771.90 trillion, respectively. The paper concludes that any adaptation policy requires settling this debt through directly financing adaptation operations in the poor countries of the Global South and effective mitigation (de-growth) policies in the Global North.

Closing the Intention-Action Gap: Behaviorally-Aligned Strategies for Effective Plastic Pollution Reduction

This brief provides an overview of existing or proposed policies that aim to mitigate plastic pollution in four countries across the Americas (Argentina, Brazil, Canada, and Jamaica), as well as how a behavioral framework can be used to analyze and develop effective plastic pollution mitigation policies. At times, policy efforts do not always produce the desired outcomes, resulting in an intention-action gap, which is a disconnect between policy intentions and the everyday behavior of stakeholders. This brief highlights behaviorally-aligned recommendations to reduce the impact of plastic waste and pollution. The ultimate goal of this policy brief is to provide policymakers an insight into behaviorally-aligned policy perspectives to address plastic pollution in their respective countries.

An evaluation of New York state livestock carbon offset projects under California’s cap and trade program

Carbon offsets allow greenhouse gas emitters in a cap and trade system to purchase the right to emit by paying others who are not subject to the cap to reduce their emissions. California has implemented a state-wide cap and trade system and developed a compliance offset program that defines protocols for offset project eligibility and methodology for calculating emissions reductions. One such protocol allows livestock methane digester projects to generate offset credits, including in other jurisdictions outside of California. Here, we assess incentives and barriers to participation in the livestock offset compliance program among dairy farmers in New York state and evaluate who is participating in this program and why. Our research presents a novel assessment of this carbon offset protocol and contributes to our understanding of the factors that shape effective cross-boundary climate mitigation policy implementation.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.

Greenhouse gas (GHG) emissions in Australian states and territories: Determinants and policy implications

In order to implement effective climate mitigation policies, it is imperative to understand the determinants of GHG emissions. Our research indicates, no state and territory level analysis of Australia, for the determinants of GHG emissions has yet been carried out. This paper identifies the main determinants that affect GHG emissions growth in Australia and assesses their impact in the main Australian states and territories. It performs a rigorous statistical analysis and contrasts the significance of determinants using Feasible Generalised Least Squares (FGLS) Regression and the Linear Panel Data Model with Random effects for the period 1990–2018 for seven states and territories of Australia. We find a mix of GHG determinants in being significant for different states and territories while some show none of the determinants as being significant. Environmental policy analysis is later carried out and then compared with the empirical findings of this study. It is found that it is only in the latter half of the period under observation that some states and territories have instituted encouraging climate change policies while the rest lag behind. Heterogeneous climate mitigation policies, at state and territory level, will have to be implemented to decouple the significance of GHG emissions from its determinants. Also, the ready and comprehensive availability of data for unique variables, such as Savanna burning, will give clearer direction to heterogeneous and customized climate change policy solutions. Lastly, climate mitigation success in TAS (Tasmania), could serve as a leading case study to institute similar renewable energy measures in other states and territories.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

SEINN: A deep learning algorithm for the stochastic epidemic model.

Stochastic modeling predicts various outcomes from stochasticity in the data, parameters and dynamical system. Stochastic models are deemed more appropriate than deterministic models accounting in terms of essential and practical information about a system. The objective of the current investigation is to address the issue above through the development of a novel deep neural network referred to as a stochastic epidemiology-informed neural network. This network learns knowledge about the parameters and dynamics of a stochastic epidemic vaccine model. Our analysis centers on examining the nonlinear incidence rate of the model from the perspective of the combined effects of vaccination and stochasticity. Based on empirical evidence, stochastic models offer a more comprehensive understanding than deterministic models, mainly when we use error metrics. The findings of our study indicate that a decrease in randomness and an increase in vaccination rates are associated with a better prediction of nonlinear incidence rates. Adopting a nonlinear incidence rate enables a more comprehensive representation of the complexities of transmitting diseases. The computational analysis of the proposed method, focusing on sensitivity analysis and overfitting analysis, shows that the proposed method is efficient. Our research aims to guide policymakers on the effects of stochasticity in epidemic models, thereby aiding the development of effective vaccination and mitigation policies. Several case studies have been conducted on nonlinear incidence rates using data from Tennessee, USA.

Where to leave fossil fuels underground? A multi-criteria analysis to identify unburnable carbon areas in the Ecuadorian Amazon region

Despite the ongoing impacts of climate change around the world, fossil fuels continue to drive the global economy. The socio-environmental impacts of oil development at the local level are widely recognized, especially in high biocultural diversity areas, highlighting the need to develop and implement effective policies that protect both biodiversity and human rights. In consideration of the estimated remaining carbon budget to limit global warming at 1.5 °C, as well as Ecuador’s past attempts at limiting carbon extraction through the Yasuni-ITT Initiative, we adopt a new framework to identify ‘unburnable carbon areas’ with the goal of eventually phasing out fossil fuels. In the Ecuadorian Amazon—one of Earth’s high-biodiversity wilderness areas and home to uncontacted indigenous populations—50 years of widespread oil production is jeopardizing tropical ecosystems. Using the Ecuadorian Amazon as a paradigmatic case study, our research explores the feasibility of implementing energy transition paths based on unburnable carbon areas through spatial multicriteria decision analysis that is based on different approaches to territory management. We modeled interactions between oil development and areas with high biocultural sensitivities using environmental, socio-cultural, and oil-related geospatial information. We found that, for all simulations, concessions that should remain unburnable are mainly located in the south-central sector of Ecuadorian Amazon, surrounding the Yasuní National Park and the intangible zone for uncontacted indigenous people, where no reserves have been identified and oil infrastructure (wells, pipelines, etc) has not been deployed. In the Northern sector, particularly along the ‘Auca’ oil road system, the eventual continuation of fossil production requires best practices to minimize environmental impacts and respect human rights. Our spatial multicriteria approach based on geographical criteria can be replicated in different place contexts to explore different scenarios for effective climate mitigation policies.

Exploring key sectors of CO2 emissions and driving factors to spatiotemporal evolution in China from multiple perspectives.

Identifying CO2 emission from different perspectives is necessary for developing the effective mitigation policies for China. Previous studies mainly focus on exploring important sectors from production and consumption sides, while the perspective of betweenness has been neglected. For narrowing the gap, a new perspective for accounting the critical transmission sectors is discussed. In this study, we calculated and compared the CO2 emissions of production-based, consumption-based, and betweenness-based from 2012 to 2017 based on the multi-regional input-output (MRIO) model. A structural decomposition analysis (SDA) is conducted to uncover the driving forces of CO2 emissions change from three accounting principles. The Findings are as follows: (1) the heavy industry sector (559.26 Mt) in Shandong and Jiangsu (471.97 Mt), Power in Guangdong (83.77 Mt) and Beijing (199.24 Mt), Equipment in Jiangsu (213.88 Mt) are identified as the key transmission sectors; (2) the emission intensity effect and the final demand product structure effect contribute to CO2 emission decrease in China, which are largely offset by the structure effect of final demand source and the final demand scale effect. Based on this, we propose some typical policy implications, such as paying close attention to the production efficiency of the key transmission sectors, optimizing the intermediate product input structure and increasing investment in the technology level, and then reducing the intensity of carbon emission.

Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

Recent years have seen an increased interest in demand-side mitigation of greenhouse gas emissions. Despite the oftentimes spatial nature of emissions research, links to social factors and infrastructure are often not analysed geographically. To reach substantial and lasting emission reductions without further disadvantaging vulnerable populations, the design of effective mitigation policies on the local level requires considerations of spatial and social inequalities as well as the context of well-being. Consequently, we explore spatial variations in the links between consumption-based transport emissions with infrastructural factors, such as workplace distance and public transport density, and with risk-factors of transport poverty, including income, age, ethnicity, mobility constraints in London. We find that linear models report significant spatial autocorrelation at p ≤ 0.01 in their model residuals, indicating spatial dependency. Using geographically weighted regression models improves model fits by an adjusted R2 value of 9–70% compared to linear models. Here, modelling flight emissions generally sees the lowest improvements, while those models modelling emissions from cars and vans see the highest improvements in model fit. We conclude that using geographically weighted regression to assess the links between social factors and emissions offers insights which global, linear models overlook. Moreover, this type of analysis enables an assessment of where, spatially, different types of policy interventions may be most effective in reducing not only emissions, but transport poverty risks. Patterns of spatial heterogeneity and policy implications of this research are discussed.

Human and economic impacts of natural disasters: can we trust the global data?

Reliable and complete data held in disaster databases are imperative to inform effective disaster preparedness and mitigation policies. Nonetheless, disaster databases are highly prone to missingness. In this article, we conduct a missing data diagnosis of the widely-cited, global disaster database, the Emergency Events Database (EM-DAT) to identify the extent and potential determinants of missing data within EM-DAT. In addition, through a review of prominent empirical literature, we contextualise how missing data within EM-DAT has been handled previously. A large proportion of missing data was identified for disasters attributed to natural hazards occurring between 1990 and 2020, particularly on the economic losses. The year the disaster occurred, income-classification of the affected country and disaster type were all significant predictors of missingness for key human and economic loss variables. Accordingly, data are unlikely to be missing completely at random. Advanced statistical methods to handle missing data are thus warranted when analysing disaster data to minimise the risk of biasing statistical inferences and to ensure global disaster data can be trusted.

The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: Accounting for temporal influence with unobserved effect and insights from out-of-sample prediction

This paper examines the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019. While also considering the temporal stability assessment of significant factors, this study adopted a random parameters logit model with possible heterogeneity in means and variances to account for unobserved heterogeneity. Three levels of motorcyclist injury severity were considered including minor injury, severe injury, and fatal injury. Two series of likelihood ratio tests clearly indicated nontransferability between weekday, weekend, and holiday crashes and substantial temporal instability over the four-year study period. Findings also revealed many statistically significant factors that affect motorcyclist injury severity probabilities in various time-of-year and yearly models. In addition, the prediction comparison results (using out-of-sample prediction simulation) clearly illustrated substantial differences between weekday, weekend, and holiday motorcyclist injury severity probabilities, and substantial changes in each injury predicted probabilities over time. This paper highlights the importance of accounting for day-of-week and holiday transferability and temporal instability with unobserved effects in the determinants that affect motorcyclist injury severity. Through the observed nontransferability and temporal instability, the findings provide valuable knowledge for practitioners, researchers, institutions, and decision-makers to enhance highway safety, specifically motorcyclist safety, and facilitate the development of more effective motorcycle crash injury mitigation policies.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.