Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China

Recent calculations of carbon dioxide (CO₂) emissions have faced challenges because data consist of only partial information, which is called “incomplete information.” According to the emission factor method, energy consumption and CO₂ emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO₂ emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO₂ emission factor for LNG in China and calculated the corresponding CO₂ emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO₂ emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO₂ emission factors and CO₂ emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO₂ emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO₂ emissions from LNG and international fair comparison.

     

Net carbon flux from agricultural ecosystems: methodology for full carbon cycle analyses

Agricultural ecosystems have the potential to sequester carbon in soils by altering agricultural management practices (i.e. tillage practice, cover crops, and crop rotation) and using agricultural inputs (i.e. fertilizers and irrigation) more efficiently. Changes in agricultural practices can also cause changes in CO2 emissions associated with these practices. In order to account for changes in net CO2 emissions, and thereby estimate the overall impact of carbon sequestration initiatives on the atmospheric CO2 pool, we use a methodology for full carbon cycle analysis of agricultural ecosystems. The analysis accounts for changes in carbon sequestration and emission rates with time, and results in values representing a change in net carbon flux. Comparison among values of net carbon flux for two or more systems, using the initial system as a baseline value, results in a value for relative net carbon flux. Some results from using the full carbon cycle methodology, along with US national average values for agricultural inputs, indicate that the net carbon flux averaged over all crops following conversion from conventional tillage to no-till is -189 kg C ha(-1) year(-1) (a negative value indicates net transfer of carbon from the atmosphere). The relative net carbon flux, using conventional tillage as the baseline, is -371 kg C ha(-1) year(-1), which represents the total atmospheric CO2 reduction caused by changing tillage practices. The methodology used here illustrates the importance of (1) delineating system boundaries, (2) including CO2 emissions associated with sequestration initiatives in the accounting process, and (3) comparing the new management practices associated with sequestration initiatives with the original management practices to obtain the true impact of sequestration projects on the atmospheric CO2 pool.     

Particulate,Carbon Monoxide,and Acid Emission Factors for Residential Wood Burning Stoves

Emissions from residential wood burning stoves are of Increasing concern in many areas. This concern is due to the magnitude of the emissions and the toxic and chemical characteristics of the pollutants. Recent testing of standard and new technology woodstoves has provided data for developing a family of particulate and carbon monoxide emission factor curves. This testing has also provided data illustrating the acidity of woodstove emissions. The particulate and carbon monoxide curves relate the actual stove emissions to the stove size and operating parameters of burn rate, fuel loading, and fuel moisture. Curves relating stove types to the acidity of emissions have also been constructed.

Test data show actual emissions vary from 3 to 50 grams per kilogram for particles and from 50 to 300 grams per kilogram for carbon monoxide. Since woodstove emissions are the largest single category of particulate emissions in many areas, it Is essential that these emissions be quantified specifically for geographic regions, allowing meaningful impact analysis modeling to be accomplished. Emission factors for particles and carbon monoxide are presented from several stove sizes and burn rates.

The acidic nature of woodstove emissions has been clearly demonstrated. Tests indicate woodstove flue gas condensate solutions to be predominantly in the 2.8 to 4.2 pH range. Condensate solutions from conventional woodstoves exhibited the characteristic buffering capacity of carboxylic acids when titrations were performed with a strong base. The environmental impact of buffered acidic woodstove emissions is not currently well understood; however, it is possible with the data presented here to make semi-quantitative estimates of acid emissions from particulate and carbon monoxide emission factors and wood use inventories.     

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

     

Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester,New York

Black carbon (BC), an important component of the atmospheric aerosol, has climatic, environmental, and human health significance. In this study, BC was continuously measured using a two-wavelength aethalometer (370 nm and 880 nm) in Rochester, New York, from January 2007 to December 2010. The monitoring site is adjacent to two major urban highways (I-490 and I-590), where 14% to 21% of the total traffic was heavy-duty diesel vehicles. The annual average BC concentrations were 0.76 μg/m³, 0.67 μg/m³, 0.60 μg/m³, and 0.52 μg/m³ in 2007, 2008, 2009, and 2010, respectively. Positive matrix factorization (PMF) modeling was performed using PM_2.5 elements, sulfate, nitrate, ammonia, elemental carbon (EC), and organic carbon (OC) data from the U.S. Environmental Protection Agency (EPA) speciation network and Delta-C (UVBC_370nm – BC_880nm) data. Delta-C has been previously shown to be a tracer of wood combustion factor. It was used as an input variable in source apportionment models for the first time in this study and was found to play an important role in separating traffic (especially diesel) emissions from wood combustion emissions. The result showed the annual average PM_2.5 concentrations apportioned to diesel emissions in 2007, 2008, 2009, and 2010 were 1.34 μg/m³, 1.25 μg/m³, 1.13 μg/m³, and 0.97 μg/m³, respectively. The BC conditional probability function (CPF) plots show a large contribution from the highway diesel traffic to elevated BC concentrations. The measurements and modeling results suggest an impact of the U.S Environmental Protection Agency (EPA) 2007 Heavy-Duty Highway Rule on the decrease of BC and PM_2.5 concentrations during the study period.

Implications: This study suggests that there was an observable impact of the U.S EPA 2007 Heavy-Duty Highway Rule on the ambient black carbon concentrations in Rochester, New York. Aethalometer Delta-C was used as an input variable in source apportionment models and it allowed the separation of traffic (especially diesel) emissions from wood combustion emissions.     

Risk management activities for oil and gas producers and the impact on firm value

This study analyses whether hedging activities of oil and gas firms have a significant effect on the performance of the companies. The performance of companies is proxied by Tobin’s Q and panel regression models are built to estimate the coefficients for firm value and derivative use. The speculative use of derivatives is eliminated in models by the regulations under IFRS and GAAP. The results give critical information regarding asymmetric information and signalling effect. Since the coefficient of derivatives use is negative, it shows the critical meaning of disclosures on the financial healthiness. If companies are publishing high level of hedging activities, it might be a warning for investors to avoid investing at that company. This study also seeks for explanation behind firms’ hedging decisions. To our knowledge, it is among the first studies with a wide range of region and data.

     

Stabilization of the Greenhouse Impact Caused by Anthropogenic Emissions from Nordic Countries

Abstract

The possibility of decreasing the Nordic countries’ contribution to global warming in the future is examined. Anthropogenic carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions are considered. Global average radiative forcing is used as a measure of the greenhouse impact caused by the emissions. Past emissions are included in the study because they have impact far into the future. The calculation method utilized in this study can be applied to any other country.

Two hypothetical future emission development cases are presented, and the radiative forcing caused by them is calculated. In the higher emission (case A) CO2 emissions remain above current level, while N2O and CH4 emissions decrease. In the lower emission (case B) the emissions decrease to about one–tenth of the current emissions by the year 2100.

Only if very strict emission reductions (case B) take place will the greenhouse impact of the Nordic countries return to current levels during next century. Likewise, the per capita radiative forcing of Nordic countries will remain above global average unless the emissions decrease drastically (case B) and the current population levels are used in per capita calculation.     

Heterogeneous impacts of financial development on carbon emissions: evidence from China’s provincial data

The effect of financial development on carbon emissions is a hot topic. Although some researches study the heterogeneous impacts of financial development on carbon emissions at the country level, few paper has investigated their heterogeneous relations within the same country. This paper, applying geographically and temporally weighted regression (GTWR), studies the spatial–temporal heterogeneity of the impacts of financial development on carbon emissions across China’s 30 provinces from 2003 to 2017. The results show that financial development proxied by bank credit indicators curbs carbon emissions in most provinces most of the time, while that proxied by stock market indicator exhibits nonlinear relationships in most provinces, such as U-shaped, inverse U-shaped, and inverse N-shaped. The paper concludes first that financial development proxied by different indicators may exert varied impacts on carbon emissions. Second, the impact of financial development on carbon emissions shows great heterogeneity among different provinces and different years: it may be curbing or increasing, and even it is curbing, its curbing effects differ greatly across provinces and years. Third, the impact of financial development on CO2 is not always monotonic; instead, it may be nonlinear. Regional segmentation of financial markets may explain the heterogeneity. Some policy suggestions are also given.

     

Income inequality,ecological footprint,and carbon dioxide emissions in Asian developing economies: what effects what and how?

The reduction of income inequality and environmental vulnerability is the most important factor, through which we can achieve the target of Sustainable Development Goals (SDGs). The past papers have investigated the nexus between income inequality and carbon emissions; however, the relationship between income inequality and carbon emissions along with ecological footprint has not been studied in the case of developing countries. To this end, this study analyzed the impact of income inequality on both carbon emissions and ecological footprint as well as the impact of carbon emission and ecological footprint on income inequality by using the dataset from 2006 to 2017 for the 18 Asian developing economies. This study confirmed the positive relationship between carbon emissions, ecological footprint, and income inequality under the methodology of Driscoll and Kraay (D&K) standard error approach. Specifically, a higher-income gap is destructive for environmental degradation, whereas increasing level of carbon emissions and ecological footprint also leads to rising income inequality in the investigated region. Furthermore, foreign direct investment (FDI), easy access to electricity, and population growth control income inequality, but they have a detrimental effect on both ecological footprint and carbon emissions. The empirical findings also provide some important policy implications.

     

Air quality co-benefits of subnational carbon policies

To mitigate climate change, governments ranging from city to multi-national have adopted greenhouse gas (GHG) emissions reduction targets. While the location of GHG reductions does not affect their climate benefits, it can impact human health benefits associated with co-emitted pollutants. Here, an advanced modeling framework is used to explore how subnational level GHG targets influence air pollutant co-benefits from ground level ozone and fine particulate matter. Two carbon policy scenarios are analyzed, each reducing the same total amount of GHG emissions in the Northeast US: an economy-wide Cap and Trade (CAT) program reducing emissions from all sectors of the economy, and a Clean Energy Standard (CES) reducing emissions from the electricity sector only. Results suggest that a regional CES policy will cost about 10 times more than a CAT policy. Despite having the same regional targets in the Northeast, carbon leakage to non-capped regions varies between policies. Consequently, a regional CAT policy will result in national carbon reductions that are over six times greater than the carbon reduced by the CES in 2030. Monetized regional human health benefits of the CAT and CES policies are 844% and 185% of the costs of each policy, respectively. Benefits for both policies are thus estimated to exceed their costs in the Northeast US. The estimated value of human health co-benefits associated with air pollution reductions for the CES scenario is two times that of the CAT scenario.

Implications: In this research, an advanced modeling framework is used to determine the potential impacts of regional carbon policies on air pollution co-benefits associated with ground level ozone and fine particulate matter. Study results show that spatially heterogeneous GHG policies have the potential to create areas of air pollution dis-benefit. It is also shown that monetized human health benefits within the area covered by policy may be larger than the model estimated cost of the policy. These findings are of particular interest both as U.S. states work to develop plans to meet state-level carbon emissions reduction targets set by the EPA through the Clean Power Plan, and in the absence of comprehensive national carbon policy.     

Black carbon aerosols in urban air in South Asia

We report data from a yearlong (2006–2007) study of black carbon concentrations ([BC]) measured at 5-min intervals with an Aethalometer in Karachi, Pakistan. Daily mean [BC] varied from about 1 to 15 μg m^?3. However, short-term spikes exceeding 40 μg m^?3 were common, occurring primarily during the morning and evening rush-hour periods. The [BC] values were highest during November through February, ～10 μg m^?3, and lowest during June through September, ～2 μg m^?3. Diurnal, seasonal, and day-of-the-week trends are discussed. It is demonstrated that these trends are strongly affected by meteorological patterns. A simple expression is applied to the concentration profiles to separate the effects of meteorological conditions and elucidate the underlying emissions patterns. Daily emissions varied from 14,000 to 22,000 kg of BC per day. When integrated over the year emissions for Karachi Proper were estimated at 6.7 kilometric tons per year and emissions for greater Karachi were 17.5 kilometric tons per year. Folding in the populations of each area yields BC emissions of 0.74 and 1.1 kg per person per year, respectively. Applying the model to previously collected data at Lahore, Pakistan yields emissions during November–January that are around a factor of two higher than those in Karachi, but because the BC measurements in Lahore covered only three months, no estimates of annual emissions were attempted. Given the large populations of these cities the local health impact from PM alone is expected to be severe but because of the high [BC] emissions the impact on the global climate may be equally significant.     

A new look at atmospheric carbon dioxide

Carbon dioxide is increasing in the atmosphere and is of considerable concern in global climate change because of its greenhouse gas warming potential. The rate of increase has accelerated since measurements began at Mauna Loa Observatory in 1958 where carbon dioxide increased from less than 1 part per million per year (ppm yr^?1) prior to 1970 to more than 2 ppm yr^?1 in recent years. Here we show that the anthropogenic component (atmospheric value reduced by the pre-industrial value of 280 ppm) of atmospheric carbon dioxide has been increasing exponentially with a doubling time of about 30 years since the beginning of the industrial revolution (～1800). Even during the 1970s, when fossil fuel emissions dropped sharply in response to the “oil crisis” of 1973, the anthropogenic atmospheric carbon dioxide level continued increasing exponentially at Mauna Loa Observatory. Since the growth rate (time derivative) of an exponential has the same characteristic lifetime as the function itself, the carbon dioxide growth rate is also doubling at the same rate. This explains the observation that the linear growth rate of carbon dioxide has more than doubled in the past 40 years. The accelerating growth rate is simply the outcome of exponential growth in carbon dioxide with a nearly constant doubling time of about 30 years (about 2%/yr) and appears to have tracked human population since the pre-industrial era.     

Measuring the integrated risk of China’s carbon financial market based on the copula model

Measuring the risks of the carbon financial market is of great significance for investment decision-making, risk supervision, and the healthy development of the carbon trading market. Different from previous studies based on traditional VaR (value at risk), this study measures the integrated risk of China’s carbon market based on the Copula-EVT (Extreme Value Theory) -VaR model which can explore the unique strength of the copula and EVT-VaR models, of which the copula model is applied to capture the dependence between the different risk factors of carbon price volatility and macroeconomic fluctuation, while the EVT-VaR is used to explore the risk value. The empirical results show that the traditional VaR that only considers a single risk factor from carbon price volatility is likely to overestimate the risk. In addition, compared with other methods that do not consider the interdependence between risk factors, using the copula function to measure the carbon market integration risk is more effective, and backtesting also confirms this conclusion. This paper provides a specific reference for carbon emission companies to participate in the carbon market. It provides a theoretical basis for the supervision of the risk management of the carbon market.

     

Combined role of industrialization and urbanization in determining carbon neutrality: empirical story of Pakistan

A rapid process of industrialization, on the one hand, transformed the economies from agrarian to industrial societies to improve the living standards and welfare of people. On the other hand, the urbanized and industrialized economies have posed challenging threats to environmental sustainability. The query at hand is whether the growing environmental emissions are driven by industrialization and urbanization or not. This research aims to empirically examine the combined role of industrialization and urbanization in achieving carbon neutrality in Pakistan by considering foreign direct investment and economic growth as control variables in the model. The core empirical results are the following: firstly, industrialization and economic growth exhibit negative but statistically insignificant impacts on CO₂ emissions, imparting a neutral role in determining the environmental degradation in Pakistan. Secondly, urbanization and foreign direct investment disclose positive and statistically significant (at 1% level of significance) impacts on CO₂ emissions, manifesting an environmental degradation driving impact in the country. Thirdly, given the slope coefficients of urbanization and foreign direct investment (0.058 and 0.035), urbanization proved to be a stronger driver than foreign direct investment. Finally, foreign direct investment is revealed to make the Pakistani economy a “Pollution Haven” for the foreign enterprises in the country. Based on empirical results, none of the variables predicted the support for carbon neutrality in Pakistan.

     

Does tradeoff between financial and social indicators matters in environmental consideration: evidence from G7 region

The study tries to discover the impact of financial and social indicators’ growth towards environmental considerations to understand the drivers of economic growth and carbon dioxide emissions change in G7 countries. The DEA-like composite index has been used to examine the tradeoff between financial and social indicator matters in environmental consideration by using a multi-objective goal programming approach. The data from 2008 to 2018 is collected from G-7 countries. The results from the DEA-like composite index reveals that there is a mixed condition of environmental sustainability in G-7 countries where the USA is performing better and Japan is performing worse among the set of other countries. The further result shows that the energy and fiscal indicators help to decrease the dangerous gas emissions. Divergent to that, the human and financial index positively contributes to greenhouse gas emissions. Fostering sustainable development is essential to successfully reduce emissions, meet established objectives, and ensure steady development. The study provides valuable information for policymakers.

     

Industrial digitization and synergy between pollution and carbon emissions control: new empirical evidence from China

Industrial digital transformation is a key engine to help developing countries reduce pollution and carbon emissions. We used the composite system synergy model (CSSM) and modified entropy weight method to measure the degree of synergy between pollution and carbon emissions control (SPCEC) and the level of industrial digitization in each province and city based on the Chinese inter-provincial panel data from 2011 to 2020. We then used the two-way fixed effects and panel quantile regression models to test the heterogeneous influence of industrial digitization on the SPCEC. We found that: (1) industrial digitization had a positive contribution to the SPCEC. (2) Digitization of industry contributes more to the SPCEC level than the digitization of agriculture and services. (3) The promotion of SPCEC by industrial digitization is significant in the western region, but not in the eastern, central and northeastern regions. (4) In provinces and municipalities with lower level of SPCEC, the contribution of industrial digitization to the SPCEC is higher. This paper reveals the impact of industrial digitization on the SPCEC and can provide a policy reference for the realization of the SPCEC from the perspective of the integration of industry and digitization.

     

The role of green innovations,environmental policies and carbon taxes in achieving the sustainable development goals of carbon neutrality

The green innovations, environmental policies, and carbon taxes are the tools to achieve sustainable development goals (SDGs) in the mitigation process. This study is intended to examine the impact of innovation, carbon pricing (CTAX), environmental policies (EP), and energy consumption (ECON) on PM_2.5 and greenhouse gas (GHG) emission for Central-Eastern European countries. The panel effect during 2000–2018 is tested using a dynamic panel data model while the Granger causality approach obtains country-related outcomes. The outcomes reveal that eco-friendly innovations have a more profound effect on carbon mitigation. Environmental policies reduce emissions by 2.7% in the short run and 17.4% in the long run. Similarly, CTAX mitigates GHG emissions by 8.6% in the short-run and PM2.5 by 0.9% and 5.7% in the short and long run. However, urbanization, energy consumption and trade openness are the leading polluters in the region. The main findings remain dominant in the country-specific results and find unidirectional and bidirectional causality evidence among variables. The research concludes that green innovations and strict environmental policy can lead towards achieving sustainable development goals using carbon taxes as a tool on the way.

Graphical abstract

     

Evaluation of thermal optical analysis method of elemental carbon for marine fuel exhaust

The awareness of black carbon (BC) as the second largest anthropogenic contributor in global warming and an ice melting enhancer has increased. Due to prospected increase in shipping especially in the Arctic reliability of BC emissions and their invented amounts from ships is gaining more attention. The International Maritime Organization (IMO) is actively working toward estimation of quantities and effects of BC especially in the Arctic. IMO has launched work toward constituting a definition for BC and agreeing appropriate methods for its determination from shipping emission sources. In our study we evaluated the suitability of elemental carbon (EC) analysis by a thermal-optical transmittance (TOT) method to marine exhausts and possible measures to overcome the analysis interferences related to the chemically complex emissions. The measures included drying with CaSO_4, evaporation at 40–180ºC, H₂O treatment, and variation of the sampling method (in-stack and diluted) and its parameters (e.g., dilution ratio, Dr). A reevaluation of the nominal organic carbon (OC)/EC split point was made. Measurement of residual carbon after solvent extraction (TC-C_SOF) was used as a reference, and later also filter smoke number (FSN) measurement, which is dealt with in a forthcoming paper by the authors. Exhaust sources used for collecting the particle sample were mainly four-stroke marine engines operated with variable loads and marine fuels ranging from light to heavy fuel oils (LFO and HFO) with a sulfur content range of <0.1–2.4% S. The results were found to be dependent on many factors, namely, sampling, preparation and analysis method, and fuel quality. It was found that the condensed H₂SO₄ + H₂O on the particulate matter (PM) filter had an effect on the measured EC content, and also promoted the formation of pyrolytic carbon (PyC) from OC, affecting the accuracy of EC determination. Thus, uncertainty remained regarding the EC results from HFO fuels.

Implications: The work supports one part of the decision making in black carbon (BC) determination methodology. If regulations regarding BC emissions from marine engines will be implemented in the future, a well-defined and at best unequivocal method of BC determination is required for coherent and comparable emission inventories and estimating BC effects. As the aerosol from marine emission sources may be very heterogeneous and low in BC, special attention to the effects of sampling conditions and sample pretreatments on the validity of the results was paid in developing the thermal-optical analysis methodology (TOT).     

Estimating the carbon footprint of household activities in Japan from the time-use perspective

The household sector is a major driver of energy consumption and greenhouse gas (GHG) emissions. However, most existing studies have only estimated households’ carbon footprint from their expenditures. Households’ daily activity time, a scarce resource that limits and determines their consumption behavior, has rarely been integrated into the estimation. Incorporating the daily time-use patterns should thus provide a more practical perspective for mitigation policies aiming at promoting sustainable household lifestyles. In this study, by linking household time-use data and expenditure data of Japan, the carbon footprint and the GHG intensity of time of 85 daily household activities constituting the 24 hours in a day are estimated. Compared to the maximal 20-activity disaggregation in existing studies, our detailed 85-category disaggregation of daily time enables unprecedented details on the discrepancies between the carbon footprint from daily activities, many of which have previous been treated as one activity. Results indicate significant carbon mitigation potential in activities with a high GHG intensity of time, such as cooking, bathing, and mobility-related and activities. Average daily GHG emissions were also found to be higher on weekends as time-use patterns shift from paid work to free-time activities, highlighting the need for mitigation strategies on a weekly scale.

     

Effects of biodiesel made from swine and chicken fat residues on carbon monoxide,carbon dioxide,and nitrogen oxide emissions

The effects of two alternative sources of animal fat-derived biodiesel feedstock on CO₂, CO, NO_x tailpipe emissions as well as fuel consumption were investigated. Biodiesel blends were produced from chicken and swine fat waste (FW-1) or floating fat (FW-2) collected from slaughterhouse wastewater treatment processes. Tests were conducted in an unmodified stationary diesel engine operating under idling conditions in attempt to simulate slow traffic in urban areas. Significant reductions in CO (up to 47% for B100; FW-2) and NO_x (up to 20% for B5; FW-2 or B100; FW-1) were attained when using biodiesel fuels at the expense of 5% increase in fuel consumption. Principal component analysis (PCA) was performed to elucidate possible associations among gas (CO₂, CO, and NO_x) emissions, cetane number and iodine index with different sources of feedstock typically employed in the biodiesel industry. NO_x, cetane number and iodine index were inversely proportional to CO₂ and biodiesel concentration. High NO_x emissions were reported from high iodine index biodiesel derived especially from forestry, fishery and some agriculture feedstocks, while the biodiesel derived from animal sources consistently presented lower iodine index mitigating NO_x emissions. The obtained results point out the applicability of biodiesel fuels derived from fat-rich residues originated from animal production on mitigation of greenhouse gas emissions. The information may encourage practitioners from biodiesel industry whilst contributing towards development of sustainable animal production.

Implications: Emissions from motor vehicles can contribute considerably to the levels of greenhouse gases in the atmosphere. The use of biodiesel to replace or augment diesel can not only decrease our dependency on fossil fuels but also help decrease air pollution. Thus, different sources of feedstocks are constantly being explored for affordable biodiesel production. However, the amount of carbon monoxide (CO), carbon dioxide (CO₂), and/or nitrogen oxide (NO_x) emissions can vary largely depending on type of feedstock used to produce biodiesel. In this work, the authors demonstrated animal fat feasibility in replacing petrodiesel with less impact regarding greenhouse gas emissions than other sources.