Energy greenhouse gas emission inventory in Batu City

Greenhouse gas emission inventory of drinking water treatment plants and case studies in China

This paper analyzes the building process of the main greenhouse gas (GHG) emissions (CO2, CH4 and N2O) inventory from the energy sector in Palestine. The paper includes determination tools, i.e., emission factors, to estimate the amounts of national GHG emissions from sub-sectors of energy including energy industries, manufacturing industries and construction, transport and other sectors (households, agriculture and commerce and public services). The results show that the total amount of national GHG emissions from the energy sector in 2016 was 4131 thousand metric tons of CO2-equivalent (TtCO2e), which represented 0.011% of the total global GHG emissions. The average value of GHG emissions per capita from the energy sector was 0.86 tCO2e in Palestine, and its gross domestic product was estimated at 3212$/ton of CO2e. The estimated amounts of CO2, CH4 and N2O emission from the energy sector were 4022, 49 and 60 TtCO2e, respectively. The transport and household sub-sectors dominated the national GHG emissions from the entire energy sector by 58 and 32%, respectively. In general, fuels including diesel, gasoline, wood and charcoal and liquefied petroleum gas made most of the total amount of the national GHG emissions from the energy sector at 50, 18, 18 and 12%, respectively. Finally, the mitigation actions included in the first nationally determined contribution of Palestine and recommendations to help lower the national GHG emissions from the Palestinian energy sector are provided.

Urban expansion occurs in big cities in Indonesia, including Batu City. An increase in the built-up area occurred in Batu City by 554.4 ha or 2.78%, and a decrease in agricultural land by 341.1 ha occurred in 2008-2018. If the Batu City government does not pay attention to the availability of environmental services or consider the geomorphological conditions of Batu City for developing settlements. In that case, it will have an environmental impact. The environmental problem in Batu City during the 2009-2019 period was an increase in greenhouse gases by 0.75% per year. Batu City is located in a hilly area. It is necessary to explore land capability in Batu City so that land use planning follows its environmental services and is sustainable. This study aimed to determine the land capability for settlements in Batu City based on the Regulation of the State Minister for the Environment Number 17 of 2009 concerning Guidelines for Determining Environmental Supporting Capacity in Regional Spatial Planning. This study used a geographic information system (GIS) and ArcGIS 10.8 software. The method used was overlapping soil texture, slope, drainage, effective soil depth, erosion, and flood potential maps. Batu City has a slope of 30-45% and a total area of 6,581.03 ha or 33% of the area of Batu City. The largest erosion rate reached 10,326.33 ha or 52% of the total area of Batu City. Erosion occurs on land used for agriculture or moorland. Soil protection and erosion control measures are strongly recommended. The area around Batu City, 1,174.28 ha, experienced considerable erosion, and 2,631.62 ha of land in Batu City is used for settlements. Land capability analysis can determine the starting point or basis for settlement land management in Batu City, which has a slope of more than 15%. There are only 461.9 ha of land management for settlement which follows the regional spatial planning and land capability in Batu City, spread over three different districts.<script type="text/javascript" src="chrome-extension://lopnbnfpjmgpbppclhclehhgafnifija/aiscripts/t.js"></script><script type="text/javascript" src="chrome-extension://lopnbnfpjmgpbppclhclehhgafnifija/aiscripts/script-main.js"></script>

The increased number of tourists in Batu City has resulted in traffic congestion, which led to the increase of emission contributing to GHGs effect and caused global warming. According to Presidential Regulation Number 71 of 2011, each region is required to conduct a national inventory of GHGs emission, in order to determine the appropriate adaptation and mitigation strategies in reducing the GHG emission. This research aimed to reduce the GHGs emission and to determine the appropriate adaptation and mitigation strategies in Batu City especially in the transportation sector. IPCC Guidelines 2006 was used as the method to calculate GHGs emissions. Such method allowed the researchers to determine the emission level by using secondary data obtained from the relevant institution. Determination upon adaptation and mitigation strategies was on the basis of several scenarios of emission level reduction while the prioritized strategy selection was based on the Analytical Hierarchy Process method. This research revealed that the GHGs emission with business as usual scenario in 2030 contributed by transportation reached 2,072.64 Gg of CO2 while the greatest reduction of GHG emissions amounted to -6.13% taken from the scenario of Intelligent Transport System application. More importantly, the researchers figured out that the prioritized adaptation strategies should be the improvement of Urban Open Space and public transportation rejuvenation for the mitigation.

As cities are the main source of carbon emissions for human-social systems, clarifying the characteristics of carbon emission structure and distribution in urban areas is an important foundation for achieving carbon neutrality of cities and also an important challenge for human-social systems to achieve global carbon balance goals. The spatial utilization of cities is often characterized by the agglomeration of construction land, population concentration, and industrial production, with high carbon emission intensity and large total amount. The current research on the quantification of regional carbon emissions is mainly in two categories, namely, bottom-up calculation method system based on emission inventory and top-down method system based on energy balance and input-output model. However, how to clarify both the total regional carbon emissions and their spatial distribution has been a difficult problem in the field of carbon emission quantification. Based on the comprehensive consideration of these two aspects, this study tries to construct an approach that combines the top-down carbon emission measurement method with the bottom-up spatialization process. The total carbon emissions of the human-society system are specified to each land patch and, thereby, the carbon emission pattern of each emission sector in the city could be determined. In this study, we quantified the carbon emissions of Nanjing in 2020 and obtained the spatial pattern of carbon emissions in this city based on land use. The results showed that the carbon emission intensity of the main urban area of Nanjing was much higher than that of other districts, while energy consumption of the industrial production system was the main source of carbon emissions in the human-social system there. The method of this study has a relatively wide applicability and can help researchers and governments to clarify the quantity and location of their carbon emissions clearly, which is meaningful for the implementation of urban carbon reduction strategies.

Major models and data sources are reviewed that can be used for energy-conservation analysis in the residential and commercial sectors to provide an introduction to the information that can or is available to DOE in order to further its efforts in analyzing and quantifying their policy and program requirements. Models and data sources examined in the residential sector are: ORNL Residential Energy Model; BECOM; NEPOOL; MATH/CHRDS; NIECS; Energy Consumption Data Base: Household Sector; Patterns of Energy Use by Electrical Appliances Data Base; Annual Housing Survey; 1970 Census of Housing; AIA Research Corporation Data Base; RECS; Solar Market Development Model; and ORNL Buildings Energy Use Data Book. Models and data sources examined in the commercial sector are: ORNL Commercial Sector Model of Energy Demand; BECOM; NEPOOL; Energy Consumption Data Base: Commercial Sector; F.W. Dodge Data Base; NFIB Energy Report for Small Businesses; ADL Commercial Sector Energy Use Data Base; AIA Research Corporation Data Base; Nonresidential Buildings Surveys of Energy Consumption; General Electric Co: Commercial Sector Data Base; The BOMA Commercial Sector Data Base; The Tishman-Syska and Hennessy Data Base; The NEMA Commercial Sector Data Base; ORNL Buildings Energy Use Data Book; and Solar Market Development Model. Purpose; basis for model structure; policy variables and parameters; level of regional, sectoral, and fuels detail; outputs; input requirements; sources of data; computer accessibility and requirements; and a bibliography are provided for each model and data source.

As the global ambition is directed at net-zero 2050 amidst energy intensity-efficiency targets, the advanced economies, such as the United States of America (USA) has been consistently charged with more target-driven commitments. Considering this, the current study finds the influence of commercial, industrial, and household energy intensities on both the economic and environmental indicators. A set of cointegration approaches was employed to evaluate the long-run and short-run relationship between covariates and carbon emission over the period 1974–2019. Empirical findings reveal that all the covariates are positive and significantly related to carbon emissions. For instance, the emission of carbon dioxide is worsened by economic growth in both the short- and long-run. Additionally, intense use of energy across the commercial, household, and industrial sectors is responsible for an increase in environmental degradation arising from the emission of carbon emission. Importantly, environmental degradation that is attributed to energy intensity is far more (twice) in the commercial sector and household sector, than in the industrial sector. Regarding the economic aspects, there is statistical evidence that research and development expenditure in energy efficiency improves economic growth while higher energy intensities in the commercial and industrial sectors are detrimental to economic expansion. As a policy, the study suggests that the share of renewable or clean energy technology in the country’s energy mix should be significantly increased to over-turn the undesirable economic, environmental, and global warming-related issues in the United States. Other few directions for policy implication were addressed.

A scientific carbon accounting system can help enterprises reduce carbon emissions. This study took an enterprise in the Yangtze River basin as a case study. The accounting classification of carbon emissions in the life cycle of lime production was assessed, and the composition of the sources of carbon emission was analyzed, covering mining explosives, fuel (diesel, coal), electricity and high-temperature limestone decomposition. Using the IPCC emission factor method, a carbon life cycle emission accounting model for lime production was established. We determined that the carbon dioxide equivalent from producing one ton of quicklime ranged from 1096.68 kg CO2 equiv. to 1176.96 kg CO2 equiv. from 2019 to 2021 in the studied case. The decomposition of limestone at a high temperature was the largest carbon emission source, accounting for 64% of the total carbon emission. Coal combustion was the second major source of carbon emissions, accounting for 31% of total carbon emissions. Based upon the main sources of carbon emission for lime production, carbon emission reduction should focus on CO2 capture technology and fuel optimization. Based on the error transfer method, we calculated that the overall uncertainty of the life cycle carbon emissions of quicklime from 2019 to 2021 are 2.13%, 2.07% and 2.09%, respectively. Using our analysis of carbon emissions, the carbon emission factor of producing one unit of quicklime in the lime enterprise in the Yangtze River basin was determined. Furthermore, this research into carbon emission reduction for lime production can provide a point of reference for the promotion of carbon neutrality in the same industry.

Beijing faces a serious problem of carbon emissions and the economic sectors are the main source of carbon emissions. Previous literatures have extensively focused on estimating the impact of carbon emissions of individual sector. Little attention has been paid to the multisectoral carbon emissions. In this paper, a multisectoral decomposition analysis was reported to explore the carbon emissions in Beijing. The emissions are decomposed into energy structure, energy intensity, economic structure (in industry), economic output, and population scale effects by the method of logarithmic mean Divisia index. Agricultural, industrial, construction, transportation, commercial, and other sectors are taken into consideration. The results show that population scale effect is the main factor for increasing carbon emissions in all sectors. The energy efficiency improvements are primarily responsible for the decrease in emissions in the industrial sector, while it increases emissions in construction, transportation, and commercial sectors. The economic output in agricultural and other sectors exerts a positive effect on emissions. In contrast, the energy structure effect only makes a minor contribution to the emission decrease in industrial, construction, commercial, and other sectors.

The carbon emissions of sectors and households enabled by primary inputs have practical significance in reality. Considering the mutual effect between the industrial sector and the household, this paper firstly constructed an environmentally extended semi-closed Ghosh input–output model with an endogenized household sector to analyze the relationship between carbon emissions and the Chinese economy from the supply-side perspective. The structural decomposition analysis and the hypothetical extraction method were remodified to identify the supply-side driving effects of the changes in carbon emissions and investigate the net carbon linkage. The results show that the electricity, gas, and water supply sector was the key sector with the highest carbon emission intensity enabled by primary inputs. The household sector had an above 93% indirect effect of the enabled intensity, with its enabled intensity dropping significantly by more than 55% from 2007 to 2017. The operating surplus and mixed income caused 3214.67 Gt (34.17%) of the enabled emissions in 2017. The supply-side economic activity, measured by the value added per capita, was the main factor of the carbon emission growth, mainly attributed to the development of the manufacturing sector and the electricity, gas, and water supply sector. The emission intensity and allocation structure both brought a decrease in carbon emissions. The electricity, gas, and water supply sector and the manufacturing sector were the major sources of the supply-induced cross-sectoral input emissions, while the commercial and service sector and the household sector were the top source of supply-induced cross-sectoral output emissions. This paper sheds light on the policies of the carbon emission abatement and the adjustment of the allocation structure from the perspective of supply.

The selected Greater Mekong Subregion (GMS) countries namely, Cambodia, Lao PDR, Thailand, and Vietnam submitted their respective Intended Nationally Determined Contributions (INDCs) to the United Nations Framework Convention on Climate Change (UNFCCC) in 2015 and subsequently ratified the Paris Agreement. The INDCs of the selected GMS countries set the total GHG emissions reduction target in the range of 117 to 339 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq by 2030 when compared to the business-as-usual case. The GHG emissions in the INDCs are expected to be reduced through many measures in the energy sector such as efficiency improvement and renewable energy and other measures in the non-energy sector. However, among the four countries only Thailand has certain NDC roadmap for the target in 2030 while others do not have. This matter raises uncertainty on the amount of GHG emissions that can be reduced in the four countries in the INDC timeline. This study estimates the potential of the renewable energy in mitigating the GHG emissions in the selected GMS countries to see how much emissions can be cut down in view of the INDCs by using the Long-range Energy Alternative Planning (LEAP) model. The paper developed two scenarios namely, Business-as-usual (BAU) scenario and Renewable Energy Integration (REI) scenario which follows the energy policies related to renewable energy of each country. The results of the modeling indicate that by 2030 the GHG emissions in the selected GMS countries would be reduced by around 249.2 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq in the renewable energy scenario. Cambodia, Lao PDR, Thailand, and Vietnam would reduce their GHG emissions by 8.1 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq, 4.8 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq, 152.6 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq, and 83.7 Mt-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> eq respectively by 2030. The results point out that renewable energy has strong potential to achieve NDC targets.

Climate change poses a global threat to the sustainable development of human societies. If not controlled, its impacts can threaten a vast range of human life including economic, social welfare, and public health. Most of the human-made part of this phenomenon is caused by the excessive greenhouse gas emissions (GHG), particularly carbon byproducts. Several solutions have been proposed to reduce the carbon emissions. In this paper, we investigate the effectiveness of an Emission Trading System (ETS), with a case study on the European implementation of this approach. Our approach is based on the system dynamics methodology. First, we perform a literature study on the main sources of carbon emissions, and investigate the key factors involved in the carbon cycle. Then, we extract the casual relations between the derived factors and parameters. On top of the casual model, we build a stock and flow model in which the stock variables are related to their rate variables through a differential equation whose coefficients are time-varying and determined in the model itself. The whole model is reduced to a system of differential equations with variable coefficients, and is solved numerically using methods such as Runge-Kutta. The mathematical relations between the main variables are derived using regression analysis on the available historic data which are used to train the model. For the set of variables where analytic relations cannot be derived or are not suited, look-up tables are utilized. The main procedure involved in the ETS is providing an Emission Allowance (EA) trading system, by placing a price on the volumes of the emissions. Thereby, financially incentivizing the main entities that emit large amounts of CO2 (or other GHGs in equivalent volumes of CO2) to reduce their emissions. An economic model between the EA Price, Demand and Supply is derived, where the supply is determined according to the regulations (reduced by 1.74% annually), and the demand is proportional to the actual carbon emissions. All main sources of emissions such as the power sector (whose main player is the electricity demand), manufacturing industries and construction, transport sector, aviation, etc., are included in the demand side. For our case study, the data and reports of Eurostat are used and the model is simulated. A system dynamic model to determine the relations between the emissions production, demand and allowance prices is provided, which implements the method described in the EU ETS. The European data is used to simulate the model. Our simulations show that the EA pricing system can be increasingly effective to control the emissions though the EA prices, by consistently covering more industries (currently only 45% are covered) and reducing the allowance allocations. The possible implications of such a system for the US are investigated.

Optimal Methodology to Generate Road Traffic Emissions for Air Quality Modeling

In 1992, in preparation for the United Nations Conference on Environment and Development, Thailand’s first inventory of greenhouse gas emissions was completed with support from the Asian Development Bank. The 1991 version of the IPCC Methodology was used to calculate emissions. The COPATH model, developed by the Lawrence Berkeley National Laboratory, was used in the forestry sector. The base year was 1989, but data on other years are included in the paper when available. The inventory focuses on carbon dioxide, methane, nitrous oxide, carbon monoxide, nitrogen oxides, and non-methane volatile organic compounds from energy, industry, agriculture, and forestry.For the base year 1989, Thailand emitted a total of 112 Tg CO2 and 7.0 Mg CH4. Energy consumption generated the largest amount of Thailand’s anthropogenic emissions, accounting for 63% of the nation’s total carbon dioxide emissions. The transportation sector produced the largest proportion (43.5% of total carbon dioxide emissions from the energy sector), followed by the power generation sector, with 29.6%. The forestry sector produced the second largest proportion of carbon dioxide emissions, with 31.7% of the total. Rice cultivation is the main source of methane emissions in Thailand, responsible for 89.8% of total methane emissions. Livestock is the second largest source, with 7% of total methane emissions.The inventory is currently being revised with support from the U.S. Country Studies Program and the Royal Thai Government. The revised inventory will include more subsectors, such as landfills, waste treatment, and other industrial processes, and will use 1990 as the base year.

Dynamics of energy sector and GHG emissions in Saudi Arabia