The effects of energy taxes level on greenhouse gas emissions in the environmental policy measures framework

Feeding strategies and manure management for cost-effective mitigation of greenhouse gas emissions from dairy farms in Wisconsin

An Environmental Tax Towards More Sustainable Food: Empirical Evidence of the Consumption of Animal Products in France

Executive Summary: The California Climate Action Registry, which was initially established in 2000 and began operation in Fall 2002, is a voluntary registry for recording annual greenhouse gas (GHG) emissions. The purpose of the Registry is to assist California businesses and organizations in their efforts to inventory and document emissions in order to establish a baseline and to document early actions to increase energy efficiency and decrease GHG emissions. The State of California has committed to use its ''best efforts'' to ensure that entities that establish GHG emissions baselines and register their emissions will receive ''appropriate consideration under any future international, federal, or state regulatory scheme relating to greenhouse gas emissions.'' Reporting of GHG emissions involves documentation of both ''direct'' emissions from sources that are under the entity's control and indirect emissions controlled by others. Electricity generated by an off-site power source is consider ed to be an indirect GHG emission and is required to be included in the entity's report. Registry participants include businesses, non-profit organizations, municipalities, state agencies, and other entities. Participants are required to register the GHG emissions of all operations in California, and are encouraged to report nationwide. For the first three years of participation, the Registry only requires the reporting of carbon dioxide (CO2) emissions, although participants are encouraged to report the remaining five Kyoto Protocol GHGs (CH4, N2O, HFCs, PFCs, and SF6). After three years, reporting of all six Kyoto GHG emissions is required. The enabling legislation for the Registry (SB 527) requires total GHG emissions to be registered and requires reporting of ''industry-specific metrics'' once such metrics have been adopted by the Registry. The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) was asked to provide technical assistance to the California Energy Commission (Energy Commission) related to the Registry in three areas: (1) assessing the availability and usefulness of industry-specific metrics, (2) evaluating various methods for establishing baselines for calculating GHG emissions reductions related to specific actions taken by Registry participants, and (3) establishing methods for calculating electricity CO2 emission factors. The third area of research was completed in 2002 and is documented in Estimating Carbon Dioxide Emissions Factors for the California Electric Power Sector (Marnay et al., 2002). This report documents our findings related to the first areas of research. For the first area of research, the overall objective was to evaluate the metrics, such as emissions per economic unit or emissions per unit of production that can be used to report GHG emissions trends for potential Registry participants. This research began with an effort to identify methodologies, benchmarking programs, inventories, protocols, and registries that u se industry-specific metrics to track trends in energy use or GHG emissions in order to determine what types of metrics have already been developed. The next step in developing industry-specific metrics was to assess the availability of data needed to determine metric development priorities. Berkeley Lab also determined the relative importance of different potential Registry participant categories in order to asses s the availability of sectoral or industry-specific metrics and then identified industry-specific metrics in use around the world. While a plethora of metrics was identified, no one metric that adequately tracks trends in GHG emissions while maintaining confidentiality of data was identified. As a result of this review, Berkeley Lab recommends the development of a GHG intensity index as a new metric for reporting and tracking GHG emissions trends.Such an index could provide an industry-specific metric for reporting and tracking GHG emissions trends to accurately reflect year to year changes while protecting proprietary data. This GHG intensity index changes while protecting proprietary data. This GHG intensity index would provide Registry participants with a means for demonstrating improvements in their energy and GHG emissions per unit of production without divulging specific values. For the second research area, Berkeley Lab evaluated various methods used to calculate baselines for documentation of energy consumption or GHG emissions reductions, noting those that use industry-specific metrics. Accounting for actions to reduce GHGs can be done on a project-by-project basis or on an entity basis. Establishing project-related baselines for mitigation efforts has been widely discussed in the context of two of the so-called ''flexible mechanisms'' of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (Kyoto Protocol) Joint Implementation (JI) and the Clean Development Mechanism (CDM).

Hydrogen peroxide-aged biochar mitigating greenhouse gas emissions during co-composting of swine manure with rice bran.

Quantifying the impact of energy consumption sources on GHG emissions in major economies: A machine learning approach

Unveiling the past, shaping the future: Analyzing three centuries of data to explore China's trajectory towards carbon neutrality

This research aims to assess the socio-economic and environmental impacts of the Lithuanian long-term renovation strategy, focusing on improvements in the energy performance of renovated multi-apartment buildings in the country. The methodology used in the study is centred on the CleanProd general equilibrium model, which is based on publicly available data from the FIGARO database and Eurostat’s non-financial transaction statistics. The four renovation financing scenarios analysed are represented in the model by changes in the demand for energy resources and construction and other transactions related to the renovation programme. To reflect the dynamic nature of the renovation programme, counterfactual equilibria are sought for each year of the renovation programme. The results revealed that renovation of multi-apartment buildings brings net benefits, including long-term increases in gross domestic products (GDPs) and employment, as well as a decrease in economy-wide greenhouse gas (GHG) emissions, and is aligned with the binding European Union’s energy efficiency target to reduce energy consumption at least by 11.7% in 2030 (in comparison to 2020). The Increase in Taxes on Products scenario is modelled as the most favourable scenario. It assures annual GDP growth by 0.37%, employment growth by 2170 jobs a year, including 606 jobs for young people, and an annual decrease in GHG emissions by 929–1043 ktCO2eq. It is found that the most considerable benefits are received during the renovation of medium-size buildings when construction demand increases by EUR 600,000–800,000 per year and natural gas and district heating demand are reduced by EUR 59,000–187,000 per year. Other scenarios demonstrating different building renovation and energy efficiency support practices, including Costless, Reallocation of Governmental Expenditure, and Governmental Loan, show relevant but slightly lower benefits.

Aquaculture is a major source of protein in Sub-Saharan Africa (SSA), a region experiencing rapid population growth, changing lifestyles and preferences, and increased health awareness. However, the industry is still underdeveloped and is of a subsistence nature. Climate change has impacted aquaculture production (AQUAP) in SSA because of greenhouse gas (GHG) emissions. However, AQUAP activities also results in GHG emissions. In SSA, the causal effect of GHG emissions and AQUAP has not yet been empirically established and quantified. The objective of the study was to determine the relationship between GHG emissions and AQUAP in SSA. The parsimonious vector autoregressive (VAR) model was used in the study, with annual time series data of Gross Domestic Product (GDP), meat production (MP), GHG emissions, and AQUAP from 1970 to 2020. The findings demonstrate that AQUAP in SSA was suppressed until 2006 when it suddenly increased. Western and Central Africa have dominated AQUAP in SSA. GHG emissions were dropping sporadically until 1991 when they began to rise gradually. In both the long and short run, GHG emissions had a negative influence on AQUAP, while AQUAP had an asymmetric impact on GHG emissions. AQUAP impacts GDP positively in both the long and short run, and GHG emissions had an asymmetric impact on GDP. In conclusion, GHG emissions negatively affect AQUAP. In addition, AQUAP reduced GHG emissions in the short run but however increased it in the long run. This indicates the infancy of the sector in SSA, the initial phase of the Environmental Kuznets Curves (EKC). Furthermore, GDP is positively affected by both GHG emissions and AQUAP. This also cements the initial stages of the EKC, with economic development also powered by GHG emissions, with also the positive contribution of AQUAP to economic growth. Overall, the study concludes of initial economic, and aquaculture sectoral development powered by GHG emissions. However, this is also leading to increased emissions. The study recommends upscaling AQUAP in SSA given its infancy, huge economic potential, sustainability and low GHG emission potential but should be grounded on environmentally sustainable practices.

Climate change is a major threat for our planet, with major negative impacts on economies and populations. International and regional organizations, nongovernmental organizations, and governments have joined forces to take meaningful action against it. The European Union has also prioritised the fight against climate change, creating and implementing economic measures as part of its evolving environmental policy. The aim of our paper is to empirically evaluate the effects of environmental taxes on greenhouse gas (GHG) emissions in 20 European countries, in the time frame between 1994 and 2012. Our research is based on data collected from the OECD database, using panel data estimation techniques. In our paper, we analyze the direct effect of environmental taxes on GHG emissions, as well as the indirect effect resulting from environmental expenditures, which are used as control variables. The empirical results we obtained, based on fixed-effect estimates, show that the direct effect of environmental taxes (energy taxes and other taxes) on GHG emissions is negative and statistically significant. Moreover, our findings show that environmental expenditures do not have a significant impact on greenhouse gas (GHG) emissions. Overall, our paper underlines that environmental taxes are extremely important in order to reduce pollution and improve environmental quality in European countries.

The world population is expected to grow to about 10 billion in 2050. To supply the future human population with food while sustaining a liveable planet, food should be produced sustainably. One of the most urgent environmental issues is climate change, induced by greenhouse gas (GHG) emissions. The dairy sector is a large contributor to GHG emissions. Important GHGs related to milk production are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), mainly emitted during feed production, enteric fermentation, and manure management. Diseases in dairy cows can reduce milk production, reproduction performance and longevity, and increase the amount of discarded milk. The objectives of this thesis were to estimate the impact of diseases (subclinical ketosis, clinical mastitis, and foot lesions) on GHG emissions, and to understand the relation between impact of diseases on GHG emissions and economic performance. First, a dynamic stochastic simulation model was developed to simulate the dynamics of the diseases and the associated production losses (reduced milk production, discarded milk, a prolonged calving interval, and removal (culling or dying on the farm)) per cow during one lactation. This model was combined with a life cycle assessment to quantify the impact of diseases on GHG emissions per ton fat-and-protein-corrected milk (kg CO2equivalents/t FPCM) from cradle to farm gate. Processes included were feed production, enteric fermentation, and manure management. The emissions of GHGs of cows with a disease increased on average by 21 (2.3%) kg CO2e/t FPCM per case of subclinical ketosis, by 58 (6.2%) kg CO2e/t FPCM per case of clinical mastitis, by 4 (0.4%) kg CO2e/ t FPCM per case of digital dermatitis, by 39 (4.3%) kg CO2e/ t FPCM per case of white line disease, and by 33 (3.6%) kg CO2e/ t FPCM per case of sole ulcer. An economic analyses was performed to estimate the costs of subclinical ketosis and related diseases. The total costs of subclinical ketosis were €130 per case per year. Comparing the impact of production contributors from a GHG emissions and economic perspective showed that a reduction in milk production had the highest impact on the economic performance, whereas removal and discarded milk had the highest impact on increase in GHG emissions. Prevalence, pathogen type, farm management (e.g. culling, feed, and manure), and prices (e.g. milk and feed) will affect the impact of production contributors on GHG emissions and economic performance. Therefore, specific farm analyses are needed to estimate the impact of diseases for a specific dairy farm. Diseases in dairy cows increase GHG emissions by approximately 0.4 Mton per year, which equals 15% of the Dutch governmental goal of GHG emission reductions in agriculture in 2030. Reducing diseases can decrease GHG emissions, can increase the income of the farmer, and can improve animal welfare. Therefore, reducing diseases can contribute to sustainable development of the dairy sector.

Sewage treatment processes are considered an important source of greenhouse gas (GHG) emissions, particularly N2O and CH4. In rural sewage treatment processes, GHG emissions are often neglected owing to the small scale of treatment and dispersed distribution. In this study, the non-CO2 GHG emission quantity, spatiotemporal distribution characteristics, and influences factors were analyzed based on rural sewage from 2015 to 2020. Emissions from rural sewage treatment in 2020 reached 122.72 Gg (CO2-eq), comprising 69.81 Gg N2O and 52.91 Gg CH4, representing a 35.29% increase compared to 2015. There are large variations between province-level regions: more GHG was emitted from the eastern than the north-west of China. The treatment of rural domestic sewage can simultaneously purify water quality and decrease GHG emissions, and the improvement in the rate of treatment is beneficial to "carbon peak and carbon neutralization." GHG emissions from rural sewage treatment showed a positive correlation with both GDP and sewage discharge, and N2O was positively correlated with protein consumption per capita. This study would provide a theoretical basis for policy formulation, as it supplies basic data on carbon emissions for China's rural sewage treatment. SUMMARY: Rural sewage treatment (RST) plants contribute significantly to GHG emissions. N2O emission from rural sewage treatment in 2020 in China was 69.81 Gg. CH4 emission from rural sewage treatment in 2020 in China was 52.91 Gg. Large variations in GHG emissions were found between province-level regions. Domestic RST can simultaneously purify water quality and decrease GHG emissions.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China – using a life cycle assessment approach

Livestock production is under growing public and scientific scrutiny for its greenhouse gas (GHG) emissions. This article contains a preliminary assessment of the inclusion of upstream life-cycle GHG emissions in concentrated feeds design, using the most common nonlinear programming optimization algorithms to determine feed composition. First, GHG emissions are included as costs in a single criteria optimization problem. The unit price of GHG emissions was obtained using a genetic algorithm. Second, GHG emissions are included as a target function to minimize in a multi criteria optimization problem using goal attainment programming. Results obtained after both optimization methods were applied to two case studies, namely fattening pigs and rabbit feeds. Changing ingredients in concentrated feed blends has a marginal effect on GHG emissions due to mandatory nutritional constraints. If the optimization is unconstrained, the maximum possible decrease in GHG emissions is 27.5% for the pigs feed, accompanied by increasing costs and a decrease in feed nutritional quality. To maintain nutritional integrity, the maximum possible reduction in GHG emissions is 7.5%. Considering cost as an optimization variable in the problem, the maximum decreases are even lower. It is possible to decrease emissions by 71% for the rabbits feed, but the cost of the reduction is higher than the opportunity cost for farmers to reduce GHG emissions using other strategies. These results are qualitatively robust but critically depend on feed ingredients GHG emissions and cost data.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third