The inclusion or omission of cement from emissions reports can have an impact on data trends, especially for countries that produce a large amount of cement, such as China. Considering the carbon intensity of China’s energy use, Ausubel and Waggoner (2008) show how emissions reported by EIA (which does not include cement emissions) showed a slight decrease in China’s carbon intensity from 1980 to 2004. However, CDIAC (which does include cement emissions) for the same time period showed no decrease in carbon intensity. This is due to the cement production process in China becoming more energy intensive and thus more carbon intensive. According to EIA data, China has been improving its carbon intensity, yet according to CDIAC, it has not. The inclusion or omission of traditional fuels in energy statistics can lead to significant trend differences in carbon intensity analyses. Considering the carbon intensity of energy for India, the differences are quite clear. IEA-S, using IEA data that includes traditional fuels, shows a lower absolute carbon intensity (as there is much energy being produced from biomass that has no corresponding emissions accounted for), but as the share of biomass decreases and the share of other fuels increase over time, carbon intensity steadily increases. For EIA data, which does not include traditional fuels, carbon intensity has stayed relatively constant since 1990 (as noted in Figure 4 in Section II the commercial fuel mix of India has stayed relatively stable even as commercial fuels make up a larger percentage of total energy use). Thus the data reported by these two institutions lead to contradictory decarbonization trends. EIA implies progress while IEA-S implies a worsening situation. However, neither agency is entirely comprehensive and there is a third possible trend. When energy usage from traditional biomass sources are included and carbon emissions from these sources are included (using an emission factor of 109.58 g CO 2 /MJ), there is a steady decline in
It is in the use of heated tap water that the greatest variability in energy usage between households is seen and where the greatest potential savings can be made. Kenny and Gray (2009b) showed that per capita home energy use fell by 27 and 34% respectively in two and three person households compared to a single occupancy household. Tap water use is the only factor associated with water use that is significantly affected by occupancy rate. So, for example, if we assume a 30% reduction in hot tap water usage for an average household of 2.4 occupants then the annual energy usage would fall from 12,087 to 10,003 kWh (Table 3). However, in this paper we have not applied this correction factor due to a lack of actual occupancy usage data.
A proper way is for the government to initiate and develop an environmental friendly atmosphere to educate their population the importance of protecting their environment. Energy should be efficiently allocated into more productive sectors of the economy without jeopardizing their economic growth. Having stringent environmental and energy policy regulated nationally would help monitoring the agriculture sector including as well the inflows of FDI. They should include them as part of their long- term policy agenda in developing their strategic development plan. The main implication is that as incomes in these countries grow, without any form of regulated environmental policies, the countries in this region will end up increasing further their level of CO 2 emissions.
With the constant growth of global population and economic scale and the vast consumption of energy, the global catastrophic climate change caused by the rise of carbondioxide in the air appears frequently and has gravely threatened the survival and development of humans. To cope with the energy crisis and climate warming, people have long been aware of the importance of energy saving and emission reduction. Low-carbon development has become the important issue in the 21st century. During the Copenhagen World Climate Conference held in December 2009, many countries put forward their emission reduction target. The Chinese government promised before the conference that by 2020 Chinese unit GDP carbondioxideemission will drop 40-45% compared with 2005. Adhering to the low-emission sustainable development path and developing recycling economy and low-carbon economy has become an consensus of the international society. Energy consumption is the main source of greenhouse gases, so calculating the carbondioxide from energy consumption is the primary task in studying the regional low-carbon development. As the political, cultural and educational center of China, Beijing arouses high attention from all parties in energyemission reduction. In recent years, Beijing has achieved a good result in energy saving and emission reduction. However, the environmental problem is still very severe with the acceleration of the urbanization process and the rapid economic development. From the beginning of 2013, the “hazy” weather has arisen continuously and the environmental problem is put on the working agenda by the government. How to ensure the reduction of carbonemission without influencing economic development becomes an important issue in Beijing’s economic transformation process. Based on statistical analysis on energy consumption carbonemission, the paper uses the LMDI model to analyze the driving factors of the carbonemission change in Beijing, understand the contribution rate of various factors to carbonemission and provide theoretical basis for the formulation and adjustment of carbonemission reduction policies by stages.
Air temperature and precipitation from 2005 to 2014 of the study area were re- ceived from the records available at the Department of Hydrology and Meteor- ology (DHM), Nepal. Soil temperatures and soil water content at 5 cm soil depth were measured at three different points near the chamber, during each soil res- piration measurement. The soil temperature was measured with a digital lab stem thermometer (AD-5622, A&D, Japan). Similarly, the soil water content was measured with TRIME-FM (Imko, Germany). Photosynthetic Photon Flux Den- sity (PPFD, light) was measured by using a data logger with LI-190SA quantum sensor (LI-COR Inc.) placed on top of the chamber at three points during each soil respiration measurement. The PPFD was measured in October 2015 and the measurement was not possible to make in April 2016 due to some technical glitch.
The neural network structure that used for the carbon es- timation is a multi-layer feed forward network. As ex- plained before the network consists of an input layer, one hidden layer, and an output layer. The input layer consists of four inputs data the global oil, natural gas, coal, and primary energy consumption. The hidden layer function is a nonlinear and consists of 5 neurons. The hidden units
In thermodynamics, the term work denotes a means for transferring energy. Work is an effect of one system on another which is identified and measured as follows: Work is done by a system on its surrounding if the sole effect on everything external to the system could have been rising of a weight. Notice that the raising of a weight is in effect a force acting ever, the sole effect could be the change in elevation of a mass. The magnitude of the work is measured by the number of standard weights that could have been raised.
uptake was 423.06 ± 44.94 mmol/gDCW/h in the early exponential phase, and 107.5 ± 44.94 mmol/gDCW/h at the end of 7 h, suggesting that the rate reported by Kral et al.  might be measured for a late exponential phase. Lupa et al.  reported methane evolution rates (MERs) ranging from 9.40 to 27.55 mmol/gDCW/h for cell growth rates of 0.04–0.13/h, which is close to our MER of 27.19 ± 17.75 mmol/gDCW/h for a growth rate of 0.064 ± 0.049/h in the late exponential phase. Apart from these two studies, no other data have been reported in the literature for the uptake and production rates of M. maripaludis. Thus, our study is the first to give a full range of comprehensive growth and flux data for M. maripaludis. In fact, we could not find similar data for any other methanogen except for one study  on M. barkeri, which reported a maximum H 2 uptake rate
Data for the study was obtained from World Bank data base. The estimated model might suffer from errors in variables and data massaging. The study is descriptive in nature and as such causality issues are not the focus of the current study. The period for the study is between 1961- 2010. Structural breaks in unit root are not considered in the study. Other influential factors such as temperature and precipitation are not considered for non-availability of data. The rest of the study deals with the methods, empirical results conclusions and policy implications. The rest of the paper looks at the methodology, empirical results and discussion and conclusion
In the decomposition analysis carried out for the Republic of Ireland by Llop and Tol (2011), the authors found that the source of greenhouse gas emissions is concentrated in a few sectors, but that the sectoral share of emissions depends upon how environmental responsibility is defined. When environmental responsibility accounts for emissions from intermediate inputs, and not just those due final demand, the differences between sectors in terms of emission intensity are less striking. The authors conclude that environmental policies should be designed to take account of emissions from intermediate demand as well as those from final consumption. In their analysis, Liang et al. (2007) also highlight the importance of the definition of environmental responsibility chosen. In their multi-regional analysis they note the discrepancy between emissions emitted in a region and emissions caused by that region. They note that the definition of responsibility can affect whether or not a given environmental target can be realised. Liang et al. look at a potential revenue-neutral carbon tax and analyse two alternative scenarios: “emitter pays” versus “driver pays”. They find that different regions pay/benefit depending upon the definition of environmental responsibility chosen.
Although few staff and students travel by air except for conferences, sports, and other student programs, emission from such sources were considered op- tional in calculation of campus carbonemission . In view of uncertainties associated with the estimation of GHG from Air travels and because universities have no control over it, MUCET did not considered such emission. Similarly, all other indirect emissions such as upstream emissions from the production and transportation of purchased goods and procurement or upstream supply chain, business travel, students’ trips home and visitor travel were eliminated. In view of the uncertainty of calculating Scope 3 emissions accurately, the UK green league exempted it from the sector-wide carbon reduction strategy at the nation- al level pending a nationally agreed methodology for accurately calculating such emissions (Green League 2010 online). Therefore, the tool did not consider Scope 3 emissions for assessment of CO 2 emissions.
The authors wish to express our appreciation to T. J. Blasing, CarbonDioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge Na- tional Laboratory, for supplying us the source of the data and his helpful sugges- tions. We wish to thank the Faculty of Public Health, the University of Benghazi for funding the research, right with the support provided by Prof. Chris P. Tso- kos.
Reinert, K.A. & D.W. Roland-Holst (2001) studied NAFTA and industrial pollution. In this paper, the authors utilize a three-country, applied general equilibrium (AGE) model of the North American economy and data from the World Bank’s Industrial Pollution Projection System (IPPS) to simulate the industrial pollution impacts of trade liberalization under NAFTA. According to their studies they find that the most serious environmental consequences of NAFTA occur in the base metals sector. In terms of magnitude, the greatest impacts are in the United States and Canada rather than Mexico. However, the Mexican petroleum sector is also a significant source of industrial pollution, particularly in the case of air pollution. Beside petroleum sector the transportation equipment sector is also an important source of industrial pollution in Mexico. This is the case for both volatile organic compounds and toxins released into the air in Canada and the United States. Finally, the authors identified that the chemical sector is a significant source of industrial toxin pollution in the United States and Mexico, but not in Canada.
using the fixed-effect method revealed that all the study variables are positively significant except for economic growth which is only significant at a 10% significance level. This aligns with Dritsaki and Dritsaki  study results, which found that there exist a significant and positive relationships between the variables under consideration. The results of the R-squared for both the fixed effects and pooled OLS models, which are 100% and 91%, respectively, suggest that the study independent variables (i.e., energy consumption, GDP, population and capital stock) explain the variance in the CO 2 emission levels, which is the dependent variable. This means that the models are a good fit.
reaching 27 Mpkm, although its share remained equal to about 0.5% of total demand for passenger transportation. Passenger transport by sea in the European Union is particularly high in Denmark (8954 passengers per 1000 inhabitants), Greece (8720 passengers per 1000 inhabitants), Croatia (5090 passengers per 1000 inhabitants), Estonia (4782 passengers per 1000 inhabitants), Sweden (3705 passengers per 1000 inhabitants) and Finland (3215 passengers per 1000 inhabitants) with all other countries having less than 1500 passengers per 1000 inhabitants (2004 data; Eurostat, 2006). Most passenger ferry lines are offered by main seaports. The highest number of main sea ports in Europe are found in the United Kingdom (46) and Italy (43) while the lowest in Bulgaria (2), Malta (2), Lithuania (1) and Iceland (1). Greece possesses 26 main seaports with Piraeus being the top ranking port in the country. Many of these ports along with their suburbs, such as Piraeus, are areas of concentrated economic activity with corresponding high demand for transportation most of which is fulfilled with traditional means (mostly passenger car and public transportation). Sambracos (2000 and 2001) explored the economics of an interesting albeit unusual option of providing coastal passenger transport in the Piraeus area of Greece via high-speed small passenger ferries and showed that the most profitable sea itinerary was achieved when fewer mid stops and the highest ticket price were applied. This work estimates and compares the environmental impact (i.e. energy consumption and CO 2 emissions) of the proposed coastal
Data from soil respiration values of different sites were pooled and regressed against temperature and moisture contents and as a result significant linear relationships were identified. Many works have reported that temperature is the critical factor for predicting the soil respiration (Singh and Gupta, 1977; Pandey and Singh, 1981; Raich and Potter, 1995; Bijracharya et al. 2000; Saraswathi et al. 2008; Devi and Yadava, 2008). Soil respiration is controlled primarily by temperature (Lloyd and Taylor, 1994). It is due to the soil physiological processes are controlled by enzymatic rates in a greater extent than resource supply rates (Skopp et al. 1990; Craine et al. 1998). It also controlled by the SOC content and ecosystem type (Zheng et al. 2009). So the respiratory substrate present in the soil plays a crucial role in the rate of soil respiration (Liu et al. 2006).
The linkage between foreign direct investment, renewable energy, openness trade and growth for the period 1975-2011 was analyzed by Sbia et al. (2014). The authors use as empirical strategy the unit roots, ARDL bounds and VECM Granger causality. Sbia et al. (2014) show that growth and renewable energy variables are positively correlated. However, the study of Vaona (2012) shows that the correlation between renewable energy and growth is not always positively. When the author applies the Granger causality and Toda and Yamamoto approach the renewable energy causes economic growth for the Italian case. Sadorsky (2009) considers a panel data from 18 emerging economies to explain the impact of renewable energy and growth. This empirical work shows that the renewable energy has a positive impact on economic growth. Apergis and Payne (2010) analyses the causality between renewable energy and economic growth utilizing panel data from OECD countries for the period 1985- 2005. The study shows that renewable energy, real gross fixed capital, labour force and real income per capita are correlated between them.
In this paper we have shown how certain sectors may be responsible for the emission of substantially more or less greenhouse gases than is commonly attributed to them. In particular, sectors which are responsible for a significant proportion of direct emissions have been shown to be those whose outputs are used as factor inputs in the production of other goods, i.e., those in which the “induced component” is responsible for a large share of emissions. Conversely, certain sectors which are responsible for a low level of direct emissions are driving emissions in other sectors through their demand for emission‐intensive intermediate goods.
The parametric study has presented some interesting results, in some cases matching pre-conceptions and previous studies, in other cases not. It must be noted that it was found that some of the assumptions used may have affected the outcomes. Most specifically, the assumed efficiency factors may have a significant effect on the magnitude of the exergy results. A specific case study would require considerably more confidence in these factors. It was observed that the thermal efficiency of the water based system converged to, and was therefore wholly reliant on, the assumed factors in each test. The intent of this study was to investigate the trends resulting from the variation of the selected parameters; as such, the “shape” of the data will remain largely valid and a useful outcome.
This study aims to evaluate the performance of two-stage anaerobic digestion (AD) of POME with aerobic post-treatment. The single-stage AD served as a comparison for the two-stage AD. Evaluation on the AD was mainly focused on biogas production and treatment efficiency whereas the evaluation on aerobic post-treatment was emphasized on treatment efficiency and final effluent quality. Laboratory scale anaerobic digester was used to develop single-stage AD then modified into a two-stage AD. Laboratory scale bioreactors were used as activated sludge system (AS) then modified into activated sludge system with sludge recirculation (ASR) for aerobic post-treatment. The best experimental results were used to estimate the energy yield, CO 2 emission, and CO 2 emission reduction from a simulated POME treatment system.