Climate Econometrics: An Overview

Abstract. To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

Solar radiation management (SRM) has been proposed as a means to alleviate the climate impacts of ongoing anthropogenic greenhouse gas (GHG) emissions. However, its efficacy depends on its indefinite maintenance, without interruption from a variety of possible sources, such as technological failure or global cooperation breakdown. Here, we consider the scenario in which SRM—via stratospheric aerosol injection—is terminated abruptly following an implementation period during which anthropogenic GHG emissions have continued. We show that upon cessation of SRM, an abrupt, spatially broad, and sustained warming over land occurs that is well outside 20th century climate variability bounds. Global mean precipitation also increases rapidly following cessation, however spatial patterns are less coherent than temperature, with almost half of land areas experiencing drying trends. We further show that the rate of warming—of critical importance for ecological and human systems—is principally controlled by background GHG levels. Thus, a risk of abrupt and dangerous warming is inherent to the large-scale implementation of SRM, and can be diminished only through concurrent strong reductions in anthropogenic GHG emissions.

Anthropogenic greenhouse gases (GHG) emission and related global warming issues have been the focus of international communities for some time. The international communities have reached a consensus to reduce anthropogenic GHG emissions and restrain global warming. The quantitative assessment of anthropogenic GHG emissions is the scientific basis to find out the status of global GHG emission, identify the commitments of each country, and arrange the international efforts of GHG emission reduction. Currently the main assessment indicators for GHG emission include national indicator, per capita indicator, per GDP indicator, and international trade indicator etc. The introduction to the above indicators is put forward and their merits and demerits are analyzed. Based on the GHG emission data from the World Resource Institute (WRI), the US Energy Information Administration (EIA), and the Carbon Dioxide Information Analysis Center (CDIAC), the results of each indictor are calculated for the world, for the eight G8 industrialized countries (USA, UK, Canada, Japan, Germany, France, Italy and Russia), and the five major developing countries including China, Brazil, India, South Africa and Mexico. The paper points out that all these indicators have some limitations. The Indicator of Industrialized Accumulative Emission per Capita (IAEC) is put forward as the equitable indicator to evaluate the industrialized historical accumulative emission per capita of every country. IAEC indicator can reflect the economic achievement of GHG emission enjoyed by the current generations in every country and their commitments. The analysis of IAEC indicates that the historical accumulative emission per capita in industrialized countries such as UK and USA were typically higher than those of the world average and the developing countries. Emission indicator per capita per GDP, consumptive emission indicator and survival emission indicator are also put forward and discussed in the paper.

Life cycle assessment of greenhouse gas emissions for various feedstocks-based biochars as soil amendment

The agricultural sector is a small but significant contributor to the overall anthropogenic greenhouse gas (GHG) emission and a major contributor of N 2 O emission in the United States. Land management practices or systems that reduce GHG emission would aid in slowing climate change. We measured the emission of CO 2 , CH 4 , and N 2 O from three management scenarios: business as usual (BAU), maximum C sequestration (MAXC), and optimum greenhouse gas benefits (OGGB). The BAU scenario was chisel or moldboard plowed, fertilized, in a 2‐yr rotation (corn [ Zea mays L.]–soybean [ Glycine max (L.) Merr.]). The MAXC and OGGB scenarios were strip tilled in a 4‐yr rotation (corn–soybean–wheat [ Triticum aestivum L.]/alfalfa [ Medicago sativa L.]–alfalfa). The MAXC received fertilizer inputs but the OGGB scenario was not fertilized. Nitrous oxide, CO 2 , and CH 4 emissions were collected using vented static chambers. Carbon dioxide flux increased briefly following tillage, but the impact of tillage was negligible when CO 2 flux was integrated across an entire year. The soil tended to be neutral to a slight CH 4 sink under these managements scenarios. The N 2 O flux during spring thaw accounted for up to 65% of its annual emission, compared with 6% or less due to application of N fertilizer. Annual cumulative emissions of CO 2 , CH 4 , and N 2 O did not vary significantly among these three management scenarios. Reducing tillage and increasing the length of the crop rotation did not appreciably change GHG emissions. Strategies that reduce N 2 O flux during spring thaw could reduce annual N 2 O emission.

The Eocene Green River Formation in Wyoming has long served as a standard for lacustrine depositional systems. This lacustrine formation, excluding the culminating phase, was deposited in a closed hydrographic basin. The position of the boundary between lake and mudflat margin was dictated by the inflow/evaporation ratio (inflow greater than evaporation = transgression; inflow less than evaporation = regression). All members of the Green River Formation are characterized by repetitive stratification sequences. In the Tipton and Laney members, the repetitive stratification sequences are laminated, kerogen-rich carbonates with fish fossils overlain by dolostone with numerous desiccation features. In contrast, in the middle member (Wilkins Peak), the typical stratification sequence is trona (evaporate) overlain by dolostone, overlain by kerogen-rich carbonate (oil shale). All these stratification sequences can be explained as products of dynamic climate change and a consequent imbalance between inflow and evaporation which probably resulted from the earth’s processional variations. The evidence for global warming and climate change (prior to anthropogenic green house gas (GHG) emissions) is undeniable. The crucial question is, are anthropogenic GHG emissions accelerating the rate of climate change? The confluence of rising global temperature with substantial increases in GHG emissions since the beginning of the industrial revolution strongly suggests that the answer to this question is yes.

Understanding the greenhouse gas emissions from China’s wastewater treatment plants: Based on life cycle assessment coupled with statistical data

Variations of GHG emission patterns from waste disposal processes in megacity Shanghai from 2005 to 2015

This paper is primarily concerned with an evaluation of the UK national programme for carbon dioxide (COj emission reduction, in the context of the Framework Convention on Climate Change. It will assess whether the precautionary approach, as it stands, fully meets the objective of the FCCC (Article 2) [1], in the light of current scientific evidence. It will examine the approach of one developed economy, the UK, in developing its national programme and examine the major issues which may affect the attainment of emissions abatement targets. The Greenhouse Problem There is no doubt that the Earth is warming. During the last century, global temperatures have risen by about 0.5°C. Many of the warmest years have occurred since 1980. There is now growing scientific consensus that this warming is the result of an enhanced greenhouse effect, brought about by the anthropogenic emissions of greenhouse gases [2, 3, 4]. Anthropogenic emissions of greenhouse gases arise from industrial processes, factories, car exhaust fumes, fridges, cement manufacture, biomass burning and aerosols. Since the Industrial Revolution there has been an increase in the concentrations of carbon dioxide, methane, and nitrous oxide present in the atmosphere. Table 1.1. shows the atmospheric concentrations of the main greenhouse gases both before the Industrial Revolution and today. Before about 1750 atmospheric concentrations of greenhouse gases were thought to have been fairly constant, with little natural variation in the levels over a thousand years. Today, these greenhouse gases are increasing at variable rates, from between 0.25% and 4% (see table 1). Despite the various species implicated in climate change, the greatest attention has focused on CO? which accounts for over 60% of the radiative forcing since pre-industrial times [2]. Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541 494 Air Pollution Engineering and Management Table 1. Greenhouse gases concentrations pre-1750 and in 1992 [3].

Peatlands cover ~3% of the global land surface, yet they store 21 – 30% of the world’s soil organic carbon. Large areas of pristine peatland have been drained to facilitate traditional agricultural activity, leading to increased levels of anthropogenic greenhouse gas (GHG) emissions from these degraded peatlands. Currently, the ~12% global peatlands that are drained (~0.3% of global land area) account for ~4% of all anthropogenic GHG emissions. Within the EU, more than 50% of peatlands are degraded, with Germany having 92% of its peat soils drained for agriculture and forestry. Rewetting peatlands can reduce, or even reverse GHG emissions. While substantial research has focused on the effects on nutrient from peatland re-wetting in bogs, and within pristine Northern European environments, less work has been conducted on central European fens, and on the effect of rewetting on nitrogen in previously drained and nitrogen rich agricultural sites. We investigated the effect of three different landuses (high-, low-intensity paludiculture, wet wilderness) and two different nitrogen (N) levels on CH4 emissions from 14 different fens, located in Germany, the Netherlands and Poland, to determine landuse management optima and thresholds for reduction in GHG emissions from rewetted, formerly deeply drained agricultural peatlands. We found the highest CH4 fluxes to occur during Summer and Autumn, and lowest fluxes during Winter, across all landuses and nitrogen (N)-levels. While CH4 did significantly vary at some sites on a diurnal basis, there was no clear pattern, or definite driver of diurnal CH4 fluxes. While CH4 flux significantly increased with increasing level of paludiculture at both N-levels in Germany, CH4 flux decreased with higher intensity paludiculture at the lower-N Netherlands sites, and conversely increased with higher intensity paludiculture at high-N Polish sites. These differences in treatment effect on CH4 fluxes among the different country sites highlight the complex interaction of different drivers responsible for determining CH4 fluxes from peatlands. Overall, soil phosphorous concentration was linked to higher CH4 fluxes, while bulk density was inversely related to CH4 flux. Furthermore, general additive models showed that CH4 flux increased with soil temperature and moisture, peaking at specific carbon (C):N ratios and bulk densities. This is of relevance for management strategies, as it suggests that there is the potential for manipulation of these 4 drivers within rewetted peatlands in order to reduce future CH4 fluxes. Our results highlight the importance of maintaining minimum water table levels, and maintaining N-levels below certain thresholds in order to effectively manage CH4 fluxes, and mitigate against GHG emission contributions to global warming from current and previously drained peatlands.  

Even though biomass is characterised as renewable energy, it produces anthropogenic greenhouse gas (GHG) emissions, especially from biomass logistics. Lifecycle assessment (LCA) is used as a tool to quantify the GHG emissions from logistics but in the past the majority of LCAs have been steady-state and linear, when in reality, non-linear and temporal aspects (such as weather conditions, seasonal biomass demand, storage capacity, etc.) also have an important role to play. Thus, the objective of this paper was to optimise the environmental sustainability of forest biomass logistics (in terms of GHG emissions) by introducing the dynamic aspects of the supply chain and using the geographical information system (GIS) and agent-based modelling (ABM). The use of the GIS and ABM adds local conditions to the assessment in order to make the study more relevant. In this study, GIS was used to investigate biomass availability, biomass supply points and the road network around a large-scale combined heat and power plant in Naantali, Finland. Furthermore, the temporal aspects of the supply chain (e.g., seasonal biomass demand and storage capacity) were added using ABM to make the assessment dynamic. Based on the outcomes of the GIS and ABM, a gate-to-gate LCA of the forest biomass supply chain was conducted in order to calculate GHG emissions. In addition to the domestic biomass, we added imported biomass from Riga, Latvia to the fuel mixture in order to investigate the effect of sea transportation on overall GHG emissions. Finally, as a sensitivity check, we studied the real-time measurement of biomass quality and its potential impact on overall logistical GHG emissions. According to the results, biomass logistics incurred GHG emissions ranging from 2.72 to 3.46 kg CO2-eq per MWh, depending on the type of biomass and its origin. On the other hand, having 7% imported biomass in the fuel mixture resulted in a 13% increase in GHG emissions. Finally, the real-time monitoring of biomass quality helped save 2% of the GHG emissions from the overall supply chain. The incorporation of the GIS and ABM helped in assessing the environmental impacts of the forest biomass supply chain in local conditions, and the combined approach looks promising for developing LCAs that are inclusive of the temporal aspects of the supply chain for any specific location.

Climatic temperature controls the geographical patterns of coastal marshes greenhouse gases emissions over China

_ Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions (Well-to-Wake). These GHG emissions must be reduced by at least 50% in absolute values by 2050 to contribute to the ambitions of the Paris Agreement signed in 2015. Switching to zero-carbon fuels made from renewable sources (hydro, wind, or solar) is seen by many as the most promising option to deliver the desired GHG reductions. However, renewable energy is a scarce resource that gives a much larger GHG reduction spent within other sectors. This study explores how to reach the IMO 2050 GHG targets exclusively through energy efficiency measures. The results indicate that by combining wind-assisted ship propulsion (WASP) with a slender hull form, fuel consumption and GHG emissions can be reduced by 30–35%, at a negative abatement cost for speeds exceeding 8 knots. Where the cost saving increases with the speed because at higher speeds, the fuel accounts for a higher share of the total cost, which implies that the cost saving goes from zero at 8 knots, to 5% reduction at 11 knots average speed to 14% reduction of total cost with 15 knots average speed. In comparison, GHG reductions through zero-carbon fuels will increase transport costs by 50–200%. Introduction From the first days of our civilization, sea transport has enabled regional and global trades. Today, sea transport accounts for 80% of the global trade measured in ton-miles (UNCTAD 2021) and 3% of greenhouse gas (GHG) emissions measured Well-to-Wake (Lindstad et al. 2021). More than 40% of this sea trade is performed by dry bulkers, making them the real workhorses of the sea. Even though sea transport is energy efficient compared to other transport modes, all sectors need to reduce their GHG emissions by at least 50% in absolute values by 2050 to contribute to the Paris Agreement (UNFCCC 2015). According to Bouman et al. (2017), the desired energy and GHG reductions can be achieved through: Design and other technical improvements of ships; Operational improvements; Fuels with zero or low GHG footprints; or a combination of these.

Greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄) from human activities, are the primary drivers of global warming. Additionally, methane contributes to ozone formation and therefore contributes to air pollution, posing risk to human health. Agriculture is a significant contributor to the global GHG emissions, with methane primarily emitted through enteric fermentation in livestock and manure management practices, while carbon dioxide largely arises from the use of machinery in various land management operations. Hence, to better understand and represent the intra - annual variability of GHG emissions within the agricultural sector, it is crucial to obtain spatial and temporal information about all contributing activities.Within the ARTEMIS project we are further developing and refining a dynamic emission model to capture the spatio-temporal variability of anthropogenic GHG and air pollutant emissions in Germany and its surroundings. Inside the emission model the spatial allocator estimates the total yearly emissions with the gridded GHG emission inventory of TNO - CAMS for Europe and UBA - GRETA for Germany.To account for temporal variability, different agricultural emission activities are parameterized individually. The temporal emission distribution for machinery use during land management operations gets estimated by deriving the emission timings from phenology observation data from the German Weather Service as well as using remote sensed phenology data from the COPERNICUS project. Additionally we incorporate an agricultural timer (Ge et al. 2020, 2022) developed to estimate the start of the growing season, which allows us to derive key dates such as sowing and manure application. The temporal variability of methane emissions from enteric fermentation are parameterized using literature-based emission factors linked to livestock feed intake and animal population data from national statistical agencies.These emission datasets were integrated into a LOTOS-EUROS model simulation to demonstrate their added value. The comparison using the new dynamic emission model indicated an improved representation of intra-annual GHG concentration variability. Furthermore also the depiction of the diurnal concentration cycle showed a better alignment with measured concentrations. Additionally, evaluation against ICOS tall tower measurements revealed improvements in correlation (up to 0.06) and reductions in root mean squared error (up to 15%) between modeled and observed concentrations at nearly all stations. These findings highlight the importance of disentangling the agricultural GHG emissions into seperate subsectors, enabling a more accurate depiction of temporal variability in anthropogenic emissions. We conclude that further improving the spatio-temporal emission information should be extended on other sectors such as the industry and energy, the road traffic or the landfills as well.