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This paper examines the life-cycle greenhouse gas (GHG) emissions reduction and associated marginal costs (MC) in Japan when the government enhances the transition to battery electric passenger vehicles (BEVs) by implementing a rapid investment regime for charging stations and fossil-fueled vehicle bans. Important factors affecting BEV emissions are battery performance, battery production technology, electricity generation mix and battery minimum state of charge (SOC min). We find that in addition to a rapid investment regime, a fossil-fueled vehicles ban policy is necessary to significantly increase the uptake of BEVs and achieves the deepest reduction in life cycle GHG emissions. Under this policy, with carbon neutral electricity by 2050 and a low SOC min charging regime, life-cycle GHG emissions are reduced by 48% and the MC by 53% in 2060. A carbon neutral light transport fleet in 2050 will require carbon capture and storage to offset 24 million tonnes-CO 2 equivalent of emissions even if electricity is carbon neutral by 2050 and users adhere to a low SOC min charging regime. [Display omitted] • Enhanced infrastructure development has a little impact on the uptake of BEVs. • The more stringent vehicle ban reduces life cycle GHG emissions the most. • Battery charging regime is crucial to GHG emissions reduction and associated cost. • Improving battery and vehicle production is crucial to GHG emissions reduction. • Carbon neutral light vehicle transport by 2050 requires carbon capture and storage. [ABSTRACT FROM AUTHOR]

This study considers the emissions implications of co-combusting imported ammonia in coal-fired power stations. The study adopts a supply chain approach, estimating the emissions reduction potential of 20% ammonia co-combustion in coal-fired power stations in the country of use, and the emissions associated with ammonia production in the country of origin. The paper considers the emissions implications of using different ammonia production technologies: Haber-Bosch (HB) ammonia production with hydrogen generated from steam methane reforming using natural gas (SMR), with and without carbon capture and storage (CCS); Haber-Bosch ammonia production with hydrogen generated from renewable sources; and fully renewable electricity generated ammonia. The empirical setting of the study is an ammonia supply chain encompassing Japan and Australia. The results show co-combustion of ammonia produced with SMR-HB provides no net benefit for the combined country emissions, as ammonia production related greenhouse emissions in Australia are equivalent to the emission reductions in Japan. In contrast, co-firing ammonia produced from fully renewable sources reduces emissions in the two countries in 2030 by 43 MT per annum. SMR-HB processes with varying levels of CCS reduce annual emissions by 16–34 MT. Based on the results, different policy options are suggested for managing the emissions associated with a Japan-Australia ammonia supply chain. • Japan actively pursuing ammonia/coal co-combustion to reduce power sector greenhouse gas emissions. • An Australia-Japan ammonia supply is examined at a country boundary emissions basis. • Choice of ammonia manufacturing approach has significant impact on emissions. • 40 MT pa saving in Japan offset by 39 ± 11 MT pa increase in Australia from Best Available Technology. • Only a fully renewable ammonia manufacturing process reduces emissions in both Australia and Japan. [ABSTRACT FROM AUTHOR]

Organisations attempt to contribute their share towards fighting the climate crisis by trying to reduce their emission of greenhouse gases effectively towards net zero. An instrument to guide their reduction efforts is internal carbon pricing. Next to choosing the right pricing tool, defining the exact value of an internal carbon price, especially against the background of potential regulatory external carbon prices, and assessing its impact on business units' energy systems poses a challenge for organisations. The academic literature has so far not examined the impact differences of an internal carbon price across different countries, which this paper addresses by using an optimisation model. First, it analyses the energy system cost increase of a real-world facility based on an internal carbon price compared to a potential regulatory carbon price within a country. Second, we evaluate the energy system cost increase based on an internal carbon price across different countries. The results show that with regard to internal carbon prices the additional total system cost compared to potential external carbon prices stays within 9%, 15%, and 59% for Germany, Japan, and the United Kingdom, respectively. The increase in the energy system cost in each country varies between 3% and 93%. For all countries, the cost differences can be reduced by allowing the installation of renewables. The integration of renewables via energy storage and power-to-heat technologies depends on the renewable potentials and the availability of carbon capture and storage. If organisations do not account for these differences, it might raise the disapproval of internal carbon prices within the organisation. [ABSTRACT FROM AUTHOR]

• We estimate parameter and predictive uncertainty of a soil respiration model in a cool-temperate forest ecosystem based on Bayesian inversion approach. • We use a Gaussian innovative residual error model with a generalised likelihood function that accounts for correlated, heteroscedastic, and non-normally distributed residual error. • We demonstrate that the Bayesian inversion approach is useful as a means by which to evaluate effectively parameter and predictive uncertainty of a soil respiration model with precise representation of residual errors. Careful modelling of soil carbon sequestration is essential to evaluate future terrestrial feedback to the earth climate system through atmosphere–surface carbon exchange. Few studies have evaluated, in bio- and geo-applications, parameter and predictive uncertainty of soil respiration models by considering the difference between observations and model predictions; i.e. residual error, which is assumed neither to be independent nor to be described by a normal (i.e. Gaussian) probability distribution with a mean of zero and constant variance. In this paper, we use 2-year observations of soil carbon flux from 2017 to 2018 (hereafter referred to as 'long-term simulation') obtained with two open-top chambers to estimate parameter and predictive uncertainty of a simple soil respiration model based on Bayesian statistics in a cool-temperate forest in western Japan. We also use a Gaussian innovative residual error model in which a generalised likelihood uncertainty estimation that accounts for correlated, heteroscedastic, non-normally distributed (i.e. non-Gaussian) residual error flexibly handles statistics varying in skewness and kurtosis. Results show that the effects of correlation and heteroscedasticity were eliminated adequately. Additionally, the posterior distribution of the residuals had a pattern intermediate to those of Gaussian and Laplacian (or double-exponential) distributions. Consequently, the predicted soil respiration rate, and range of uncertainty therein, well-matched the observational data. Furthermore, we compare results of parameter and predictive inference of the soil respiration model from the long-term simulation with those constrained of short-term simulations (i.e. 4-month subsets of the 2-year dataset) to determine the extent to which the approach used affects the estimation of parameter and predictive uncertainty. No significant difference in parameter estimates was found between the long-term simulation versus any of the short-term simulations, whereas short-term simulation analysis of the uncertainty at 50 %—i.e. between the lower (25 %) and upper (75 %) quartiles of the probability range—indicated distinctive variations in model parameters in summer when more vigorous activity of trees and organisms promotes carbon cycling between the atmosphere and ecosystem. Overall we demonstrate that the Bayesian inversion approach is useful as a means by which to evaluate effectively parameter and predictive uncertainty of a soil respiration model with precise representation of residual errors. [ABSTRACT FROM AUTHOR]