Greenhouse Gas Emissions Quantification and Reduction Efforts in a Rural Municipality

The present article aims to determine the current carbon footprint (CF) of Zernez, a Swiss mountain village, and to identify reduction potentials of greenhouse gas (GHG) emissions. For this purpose, material and energy flows were assessed mainly based on detailed household surveys, interviews, and energy bills, but also by means of other information sources, for example, national statistics, traffic censuses, and literature values. To set up the GHG balance, special attention was paid to the consistent definition of system boundaries by adopting two fundamentally different perspectives: purely geographical accounting (PGA) and the consumption‐based footprint (CBF) method. Each of these two perspectives total approximately 10 tonnes of carbon dioxide equivalents per capita per year. The PGA revealed that 70% of the direct emissions in Zernez are caused by agricultural activities, whereas no consumption area dominated the consumption‐induced CF. For the identification of targeted measures, both perspectives were considered in a complementary manner. The building stock and its underlying energy supply system showed a GHG reduction potential of 80%. The building sector was thus detected as a reasonable first step for the municipality to adopt GHG mitigation strategies. In the case of Zernez, building‐stock‐related measures are predicted to decrease the current CF by 13% (CBF) and 17% (PGA), respectively.     

Energy Use and Greenhouse Gas Emissions of Air‐Source Heat Pump and Innovative Ground‐Source Air Heat Pump in a Cold Climate

This article compares climate impacts of two heat‐pump systems for domestic heating, that is, energy consumption for space heating of a residential building. Using a life cycle approach, the study compared the energy use and greenhouse gas (GHG) emissions of direct electric heating, a conventional air‐source heat pump, and a novel ground‐source air heat pump innovated by a citizen user, to assess whether such user innovation holds benefit. The energy use of the heat pumps was modeled at six temperature intervals based on duration curves of outdoor temperature. Additionally, two heat pump end‐of‐life scenarios were analyzed. Probabilistic uncertainty analysis was applied using a Monte Carlo simulation. The results indicated that, in ideal conditions, that is, assuming perfect air mixing, the conventional air‐source heat pump's emissions were over 40% lower and the ground‐air heat pump's emissions over 70% lower than in the case of direct electric heating. Although proper handling of the refrigerant is important, total leakage from the retirement of the heat‐pump appliance would increase GHG emissions by just 10%. According to the sensitivity analysis, the most influential input parameters are the emission factor related to electricity and the amount of electricity used for heating.     

Implementing Carbon‐Footprint‐Based Calculation Tools in Municipal Greenhouse Gas Inventories

Policy makers and practitioners working on local climate action continuously strive for improved greenhouse gas (GHG) inventories to support their mitigation strategies. The provision of such inventories, with the sufficient detail and quality needed, has proven to be challenging. This article describes the application of a carbon‐footprint–based (CF‐based) calculation tool to Norwegian municipalities. The aim is to improve the local GHG inventory, with a particular focus on the emissions resulting from the provision of services by local governments. Through selective CF analysis, we aim to illustrate the knowledge gained from implementing these tools in local climate action plans and to further contrast CF‐based calculations with more traditional production‐based accounting. Municipalities report they have revised their understanding of the most important GHG mitigation strategies after implementing this tool.     

Improvements in Life Cycle Energy Efficiency and Greenhouse Gas Emissions of Corn-Ethanol

Corn-ethanol production is expanding rapidly with the adoption of improved technologies to increase energy efficiency and profitability in crop production, ethanol conversion, and coproduct use. Life cycle assessment can evaluate the impact of these changes on environmental performance metrics. To this end, we analyzed the life cycles of corn-ethanol systems accounting for the majority of U.S. capacity to estimate greenhouse gas (GHG) emissions and energy efficiencies on the basis of updated values for crop management and yields, biorefinery operation, and coproduct utilization. Direct-effect GHG emissions were estimated to be equivalent to a 48% to 59% reduction compared to gasoline, a twofold to threefold greater reduction than reported in previous studies. Ethanol-to-petroleum output/input ratios ranged from 10:1 to 13:1 but could be increased to 19:1 if farmers adopted high-yield progressive crop and soil management practices. An advanced closed-loop biorefinery with anaerobic digestion reduced GHG emissions by 67% and increased the net energy ratio to 2.2, from 1.5 to 1.8 for the most common systems. Such improved technologies have the potential to move corn-ethanol closer to the hypothetical performance of cellulosic biofuels. Likewise, the larger GHG reductions estimated in this study allow a greater buffer for inclusion of indirect-effect land-use change emissions while still meeting regulatory GHG reduction targets. These results suggest that corn-ethanol systems have substantially greater potential to mitigate GHG emissions and reduce dependence on imported petroleum for transportation fuels than reported previously.     

Explaining the Variation in Greenhouse Gas Emissions Between Households: Socioeconomic,Motivational, and Physical Factors

Consumption‐accounted greenhouse gas (GHG) emissions (GHGEs) vary considerably between households. Research originating from different traditions, including consumption research, urban planning, and environmental psychology, have studied different types of explanatory variables and provided different insights into this matter. This study integrates explanatory variables from different fields of research in the same empirical material, including socioeconomic variables (income, household size, sex, and age), motivational variables (proenvironmental attitudes and social norms), and physical variables (dwelling types and geographical distances). A survey was distributed to 2,500 Swedish households with a response rate of 40%. GHGEs were estimated for transport, residential energy, food, and other consumption, using data from both the survey and registers, such as odometer readings of cars and electricity consumption from utility providers. The results point toward the importance of explanatory variables that have to do with circumstances rather than motivations for proenvironmental behaviors. Net income was found to be the most important variable to explain GHGEs, followed by the physical variables, dwelling type, and the geographical distance index. The results also indicate that social norms around GHG‐intensive activities, for example, transport, may have a larger impact on a subject's emission level than proenvironmental attitudes.     

Exploring Greenhouse Gas‐Mitigation Strategies in Chinese Eco‐Industrial Parks by Targeting Energy Infrastructure Stocks

China has more than 1,500 industrial parks, which, collectively, play a crucial role in facilitating industrialization and urbanization. A key characteristic of these parks is that most rely on shareable energy infrastructure, an efficient configuration that can also deliver substantial and sustainable reductions in greenhouse gas (GHG) emissions. This study offers strategies for mitigating GHG emissions from Chinese industrial parks. We focus on extensive data collection for the 106 industrial parks listed in the national demonstration eco‐industrial park (EIP) program. In doing so, we carefully examine the evolution of 608 serviceable energy infrastructure units by vintage year, fuel type, energy output, and technologies of combined heat and power units. We assess direct GHG emissions from both energy infrastructure and the parks, and then identify the features and driving forces of energy infrastructure development in the EIPs. We also offer recommendations for ways to mitigate the GHG emissions from these industrial parks. The energy infrastructure stocks in Chinese EIPs are characterized by heavy coal dependence (87% of capacity) and high ratios of direct GHG emissions versus the total direct emissions of the park (median value: 75.2%). These findings establish a baseline from which both technology and policy decisions can then be made in an informed way.     

Impact of Power Generation Mix on Life Cycle Assessment and Carbon Footprint Greenhouse Gas Results

The mix of electricity consumed in any stage in the life cycle of a product, process, or industrial sector has a significant effect on the associated inventory of emissions and environmental impacts because of large differences in the power generation method used. Fossil‐fuel‐fired or nuclear‐centralized steam generators; large‐scale and small‐scale hydroelectric power; and renewable options, such as geothermal, wind, and solar power, each have a unique set of issues that can change the results of a life cycle assessment. This article shows greenhouse gas emissions estimates for electricity purchase for different scenarios using U.S. average electricity mix, state mixes, state mixes including imports, and a sector‐specific mix to show how different these results can be. We find that greenhouse gases for certain sectors and scenarios can change by more than 100%. Knowing this, practitioners should exercise caution or at least account for the uncertainty associated with mix choice.     

Evaluation of a Regional Approach to Standards for Plug‐in Battery Electric Vehicles in Future Light‐Duty Vehicle Greenhouse Gas Regulations

Representing the greenhouse gas (GHG) emissions attributable to plug‐in electric vehicles (PEV) in vehicle GHG emissions regulations is complex because of spatial and temporal variation in fueling sources and vehicle use. Previous work has shown that the environmental performance of PEVs significantly varies depending on the characteristics of the electricity grid and how the vehicle is driven. This article evaluates the U.S. Environmental Protection Agency's (EPA's) GHG emissions accounting methodology in current and future standards for new electrified vehicles. The current approach employed by the EPA in their 2017–2025 model year light‐duty vehicle GHG regulation is compared with an accounting mechanism where the actual regional sales of PEVs, and the regional electricity emission factor in the year sold, are used to determine vehicle compliance value. Changes to the electricity grid over time and regional vehicle sales are included in the modeling efforts. A projection of a future GHG regulation past the 2017–2025 rule is used to observe the effect of such a regional regulation. The results showed that the complexity involved in tracking and accounting for regional PEV sales will not dramatically increase the effectiveness of the regulations to capture PEV electricity‐related GHG emissions in the absence of a major policy shift. A discussion of the feasibility and effectiveness of a regional standard for PEVs, and notable examples of region‐specific regulations instated in past energy policies, is also addressed.     

An Exploration of the Relationship between Socioeconomic and Well‐Being Variables and Household Greenhouse Gas Emissions

This research reports on a multivariate analysis that examined the relationship between direct greenhouse gas (GHG) emissions and socioeconomic and well‐being variables for 1,920 respondents living in Halifax Regional Municipality, Nova Scotia, Canada, using results from the Halifax Space‐Time Activity Research Project. The unique data set allows us to estimate direct GHG emissions with an unprecedented level of specificity based on household energy use survey data and geographic positioning system–verified personal travel data. Of the variables analyzed, household size, income, community zone, age, and marital status are all statistically significant predictors of direct GHG emissions. Birthplace, ethnicity, educational attainment, perceptions of health, life satisfaction, job satisfaction, happiness, volunteering, or community belonging did not seem to matter. In addition, we examined whether those reporting energy‐efficient behaviors had lower GHG emissions. No significant differences were discovered among the groups analyzed, supporting a growing body of research indicating a disconnect between environmental attitudes and behaviors and environmental impact. Among the predictor variables, those reporting to be married, young, low income, and living in households with more people have correspondingly lower direct GHG emissions than other categories in respective groupings. Our finding that respondents with lifestyles that generate higher GHG emissions did not report to be healthier, happier, or more connected to their communities suggest that individuals can experience similar degrees of well‐being regardless of the amount of GHG emissions associated with his or her respective lifestyle.     

Increasing Precision in Greenhouse Gas Accounting Using Real‐Time Emission Factors

For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real‐time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real‐time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.     

Estimating the Greenhouse Gas Balance of Individual Gas‐Fired and Oil‐Fired Electricity Plants on a Global Scale

Life cycle greenhouse gas (LC‐GHG) emissions from electricity generated by a specific resource, such as gas and oil, are commonly reported on a country‐by‐country basis. Estimation of variability in LC‐GHG emissions of individual power plants can, however, be particularly useful to evaluate or identify appropriate environmental policy measures. Here, we developed a regression model to predict LC‐GHG emissions per kilowatt‐hour (kWh) of electricity produced by individual gas‐ and oil‐fired power plants across the world. The regression model uses power plant characteristics as predictors, including capacity, age, fuel type (fuel oil or natural gas), and technology type (single or combined cycle) of the plant. The predictive power of the model was relatively high (R² = 81% for predictions). Fuel and technology type were identified as the most important predictors. Estimated emission factors ranged from 0.45 to 1.16 kilograms carbon dioxide equivalents per kilowatt‐hour (kg CO₂‐eq/kWh) and were clearly different between natural gas combined cycle (0.45 to 0.57 kg CO₂‐eq/kWh), natural gas single cycle (0.66 to 0.85 kg CO₂‐eq/kWh), oil combined cycle power plants (0.63 to 0.79 kg CO₂‐eq/kWh), and oil single cycle (0.94 to 1.16 kg CO₂‐eq/kWh). Our results thus indicate that emission data averaged by fuel and technology type can be profitably used to estimate the emissions of individual plants.     

Evaluating the Potential for Harmonized Prediction and Comparison of Disposal‐Stage Greenhouse Gas Emissions for Biomaterial Products

The carbon footprint (CF) of biofuels and biomaterials is a barrier to their acceptance, yet the greenhouse gas emissions associated with disposing of biomaterials are frequently omitted from analyses. This article investigates whether harmonization is appropriate for calculating the importance of biomaterials’ disposal. This research shows that disposal stages could double a biomaterial's CF, or reduce it to the point that it could claim to be zero carbon. Incineration with combined heat and power coupled with on‐site energy production in the biorefinery are identified as prerequisites to being zero carbon. The article assesses the current UK waste infrastructure's ability to support a low‐carbon bio‐based future economy, and finds that presently it only achieves marginal net reductions when compared to landfill and so cannot be said to support low‐carbon biomaterials, though the article challenges the polluter pays principle where low‐carbon disposal infrastructure are not available. Reuse and recycling are shown to have the potential to offset all the emissions caused by landfill of biomaterials. However, the savings are not so great as to offset the biomaterial's upstream emissions. The study explores the ability to overcome the barriers to incorporating disposal into life cycle assessment while identifying limitations of using harmonization as an assessment method. Specifically, data availability and industry consensus are flagged as major barriers. The study also uses sensitivity analysis to investigate the influence of methodological choices, such as allowing additional reuse and recycling stages, classifying biomaterials into different types, and choosing between opposing allocation methods.     

Life Cycle Greenhouse Gas Emissions Reduction From Rigid Thermal Insulation Use in Buildings

Thermal insulation is a strategic product for reducing energy consumption and related greenhouse gas (GHG) emissions from the building sector. This study examines from a life cycle perspective the changes in GHG emissions resulting from the use of two rigid thermal insulation products manufactured and installed from 1971 to 2025. GHG emissions related to insulation production and fugitive releases of blowing agents are modeled and compared with GHG savings from reduced heating loads in North America, Europe, and Asia. Implementation of alternative blowing agents has greatly improved the carbon dioxide 100‐year equivalent (CO₂‐eq) emission performance of thermal insulation. The net average CO₂‐eq savings to emissions ratio for current extruded polystyrene (XPS) and polyisocyanurate (PIR) insulation studied was 48:1, with a broad range from 3 to 1,800. Older insulation products manufactured with chlorofluorocarbons (CFCs) can result in net cumulative GHG emissions. Reduction of CO₂‐eq emissions from buildings is governed by complex interactions between insulation thickness and placement, climate, fuel type, and heating system efficiencies. A series of charts mapping both emissions payback and net savings demonstrate the interactions between these factors and provide a basis for specific policy recommendations to guide effective insulation investments and placement.     

Sectoral Aggregation Error in the Accounting of Energy and Emissions Embodied in Trade and Consumption

Correctly accounting for the energy and emissions embodied in consumption and trade is essential to effective climate policy design. Robust methods are needed for both policy making and research—for example, the assignment of border carbon adjustments (BCAs) and greenhouse gas emission reduction responsibilities rely on the consistency and accuracy of such estimates. This analysis investigates the potential magnitude and consequences of the error present in estimates of energy and emissions embodied in trade and consumption. To quantify the error of embodied emissions accounting, we compare the results from the disaggregated Global Trade Analysis Project (GTAP 8) data set, which contains 57 sectors to results from different levels of aggregation of this data set (3, 7, 16, and 26 sectors), using 5,000 randomly generated sectoral aggregation schemes as well as aggregations generated using several commonly applied decisions rules. We find that some commonly applied decision rules for sectoral aggregation can produce a large error. We further show that an aggregation scheme that clusters sectors according to their energy, emissions, and trade intensities (net exports over output) can minimize error in embodied energy and emissions accounting at different levels of aggregation. This sectoral aggregation scheme can be readily used in any input‐output analysis and provide useful information for computable general equilibrium modeling exercises in which sector aggregation is necessary, although our findings suggest that, when possible, the most disaggregated data available should be used.     

A Spatial Analysis of Residential Greenhouse Gas Emissions in the Toronto Census Metropolitan Area

Residential greenhouse gas (GHG) emissions in the Toronto Census Metropolitan Area are spatially analyzed to determine the impact of urban form on emission-causing activities. The key finding is that over the entire region, emissions from private auto use are on par with those from fuel use for building heating. Once beyond the transit-intensive central core, private auto emissions surpass the emissions from building operations. Variation in total auto- and building-related emissions is quite significant between census tracts, ranging from 3.1 to 13.1 tonnes of carbon dioxide equivalents per year. Of all tracts, the top ten in terms of GHG emission are located in the lower-density suburbs, and their high emissions were largely due to private auto use.     

Effect of Change of Forest Carbon Storage on Net Carbon Dioxide Balance of Wood Use for Energy in Japan

This study analyzed the net carbon dioxide (CO₂) emission reductions between 2005 and 2050 by using wood for energy under various scenarios of forest management and energy conversion technology in Japan, considering both CO₂ emission reductions from replacement of fossil fuels and changes in carbon storage in forests. According to our model, wood production for energy results in a significant reduction of carbon storage levels in forests (by 46% to 77% in 2050 from the 2005 level). Thus, the net CO₂ emission reduction when wood is used for energy becomes drastically smaller. Conventional tree production for energy increases net CO₂ emissions relative to preserving forests, but fast‐growing tree production may reduce net CO₂ emissions more than preserving forests does. When wood from fast‐growing trees is used to generate electricity with gas turbines, displacing natural gas, the net CO₂ emission reduction from the combination of fast‐growing trees and electricity generation with gas turbines is about 58% of the CO₂ emission reduction from electricity generation from gas turbines alone in 2050, and an energy conversion efficiency of around 20% or more is required to obtain net reductions over the entire period until 2050. When wood is used to produce bioethanol, displacing gasoline, net reductions are realized after 2030, provided that heat energy is recovered from residues from ethanol production. These results show the importance of considering the change in carbon storage when estimating the net CO₂ emission reduction effect of the wood use for energy.     

The Concept of City Carbon Maps: A Case Study of Melbourne,Australia

Cities are thought to be associated with most of humanity's consumption of natural resources and impacts on the environment. Cities not only constitute major centers of economic activity, knowledge, innovation, and governance—they are also said to be linked to approximately 70% to 80% of global carbon dioxide emissions. This makes cities primary agents of change in a resource‐ and carbon‐constraint world. In order to set meaningful targets, design successful policies, and implement effective mitigation strategies, it is important that greenhouse gas (GHG) emissions accounting for cities is accurate, comparable, comprehensive, and complete. Despite recent developments in the standardization of city GHG accounting, there is still a lack of consistent guidelines regarding out‐of‐boundary emissions, thus hampering efforts to identify mitigation priorities and responsibilities. We introduce a new conceptual framework—based on environmental input‐output analysis—that allows for a consistent and complete reconciliation of direct and indirect GHG emissions from a city. The “city carbon map” shows local, regional, national, and global origins and destinations of flows of embodied emissions. We test the carbon map concept by applying it to the greater metropolitan area of Melbourne, Australia. We discuss the results and limitations of the approach in the light of possible mitigation strategies and policies by different urban stakeholders.     

A Hybrid‐Unit Energy Input‐Output Model to Evaluate Embodied Energy and Life Cycle Emissions for China's Economy

We develop a hybrid‐unit energy input‐output (I/O) model with a disaggregated electricity sector for China. The model replaces primary energy rows in monetary value, namely, coal, gas, crude oil, and renewable energy, with physical flow units in order to overcome errors associated with the proportionality assumption in environmental I/O analysis models. Model development and data use are explained and compared with other approaches in the field of environmental life cycle assessment. The model is applied to evaluate the primary energy embodied in economic output to meet Chinese final consumption for the year 2007. Direct and indirect carbon dioxide emissions intensities are determined. We find that different final demand categories pose distinctive requirements on the primary energy mix. Also, a considerable amount of energy is embodied in the supply chain of secondary industries. Embodied energy and emissions are crucial to consider for policy development in China based on consumption, rather than production. Consumption‐based policies will likely play a more important role in China when per capita income levels have reached those of western countries.     

Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production,Part II: Uncertainty Analysis through Sensitivity Analysis and Scenario Testing

This is the second part of a two‐article series examining California almond production. The part I article describes development of the analytical framework and life cycle–based model and presents typical energy use and greenhouse gas (GHG) emissions for California almonds. This part II article builds on this by exploring uncertainty in the life cycle model through sensitivity and scenario analysis, and by examining temporary carbon storage in the orchard. Sensitivity analysis shows life cycle GHG emissions are most affected by biomass fate and utilization, followed by nitrous oxide emissions rates from orchard soils. Model sensitivity for net energy consumption is highest for irrigation system parameters, followed by biomass fate and utilization. Scenario analysis shows utilization of orchard biomass for electricity production has the greatest potential effect, assuming displacement methods are used for co‐product allocation. Results of the scenario analysis show that 1 kilogram (kg) of almond kernel and associated co‐products are estimated to cause between ?3.12 to 2.67 kg carbon dioxide equivalent (CO₂‐eq) emissions and consume between 27.6 to 52.5 megajoules (MJ) of energy. Co‐product displacement credits lead to avoided emissions of between ?1.33 to 2.45 kg CO₂‐eq and between ?0.08 to 13.7 MJ of avoided energy use, leading to net results of ?1.39 to 3.99 kg CO₂‐eq and 15.3 to 52.6 MJ per kg kernel (net results are calculated by subtracting co‐product credits from the results for almonds and co‐products). Temporary carbon storage in orchard biomass and soils is accounted for by using alternative global warming characterization factors and leads to a 14% to 18% reduction in CO₂‐eq emissions. Future studies of orchards and other perennial cropping systems should likely consider temporary carbon storage.     

Hybrid Input‐Output Life Cycle Assessment of First‐ and Second‐Generation Ethanol Production Technologies in Brazil

A hybrid approach combining life cycle assessment and input‐output analysis was used to demonstrate the economic and environmental benefits of current and future improvements in agricultural and industrial technologies for ethanol production in Brazilian biorefineries. In this article, three main scenarios were evaluated: first‐generation ethanol production with the average current technology; the improved current technology; and the integration of improved first‐ and second‐generation ethanol production. For the improved first‐generation scenario, a US$1 million increase in ethanol demand can give rise to US$2.5 million of total economic activity in the Brazilian economy when direct and indirect purchases of inputs are considered. This value is slightly higher than the economic activity (US$1.8 million) for an energy equivalent amount of gasoline. The integration of first‐ and second‐generation technologies significantly reduces the total greenhouse gas emissions of ethanol production: 14.6 versus 86.4 grams of carbon dioxide equivalent per megajoule (g CO₂‐eq/MJ) for gasoline. Moreover, emissions of ethanol can be negative (–10.5 g CO₂‐eq/MJ) when the system boundary is expanded to account for surplus bioelectricity by displacement of natural gas thermal electricity generation considering electricity produced in first‐generation optimized biorefineries.