Innovation of Talent Training Mode for Undergraduates Majored in Energy Under the “Carbon Neutralization” Target

In September 22, 2020, Xi Jinping said at the seventy-fifth general debate of the UN General Assembly that China will enhance the national independent contribution, and strive to achieve the peak of carbon dioxide emissions by 2030, and strive to achieve carbon neutralization by 2060. Under the goal of carbon peak and carbon neutralization, the implementation of carbon emission reduction is an important strategy for China to achieve green and low-carbon development, but also faces major challenges “The Fourteenth Five Year Plan” Period is an important window period for scientific and technological innovation to realize the transformation of carbon dioxide emission growth rate. Only with the support of scientific and technological innovation can China be expected to achieve the goal of carbon peak and carbon neutralization on schedule. This paper attempts to study how to innovate the energy undergraduate talent training mode under the goal of “carbon neutral” from the aspects of the current situation of talent training, the analysis of talent training objectives, and the path analysis of talent training mode innovation.

Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling

Better accuracy in short-term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations.

A comprehensive dataset for global, regional and national greenhouse gas emissions by sector 1970–2019

To track progress towards keeping warming well below 2 °C, as agreed upon in the Paris Agreement, comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions (GHG) is required. Here we provide a dataset on anthropogenic GHG emissions 1970–2019 with a broad country and sector coverage. We build the dataset from recent releases of the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases, and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with data on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three bookkeeping models. We provide an assessment of the uncertainties in each greenhouse gas at the 90 % confidence interval (5th–95th percentile) by combining statistical analysis and comparisons of global emissions inventories with an expert judgement informed by the relevant scientific literature. We identify important data gaps: CH4 and N2O emissions could be respectively 10–20 % higher than reported in EDGAR once all emissions are accounted. F-gas emissions estimates for individual species in EDGARv5 do not align well with atmospheric measurements and the F-gas total exceeds measured concentrations by about 30 %. However, EDGAR and official national emission reports under the UNFCCC do not comprehensively cover all relevant F-gas species. Excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are larger than the sum of the reported species. GHG emissions in 2019 amounted to 59 ± 6.6 GtCO2eq: CO2 emissions from FFI were 38 ± 3.0 Gt, CO2 from LULUCF 6.6 ± 4.6 Gt, CH4 11 ± 3.3 GtCO2eq, N2O 2.4 ±1.5 GtCO2eq and F-gases 1.6 ± 0.49 GtCO2eq. Our analysis of global, anthropogenic GHG emission trends over the past five decades (1970–2019) highlights a pattern of varied, but sustained emissions growth. There is high confidence that global anthropogenic greenhouse gas emissions have increased every decade. Emission growth has been persistent across different (groups of) gases. While CO2 has accounted for almost 75 % of the emission growth since 1970 in terms of CO2eq as reported here, the combined F-gases have grown at a faster rate than other GHGs, albeit starting from low levels in 1970. Today, F-gases make a non-negligible contribution to global warming – even though CFCs and HCFCs, regulated under the Montreal Protocol and not included in our estimates, have contributed more. There is further high confidence that global anthropogenic GHG emission levels were higher in 2010-2019 than in any previous decade and GHG emission levels have grown across the most recent decade. While average annual greenhouse gas emissions growth slowed between 2010–2019 compared to 2000–2009, the absolute increase in average decadal GHG emissions from the 2000s to the 2010s has been the largest since the 1970s – and within all human history as suggested by available long-term data. We note considerably higher rates of change in GHG emissions between 2018 and 2019 than for the entire decade 2010–2019, which is numerically comparable with the period of high GHG emissions growth during the 2000s, but we place low confidence in this finding as the majority of the growth is driven by highly uncertain increases in CO2-LULUCF emissions as well as the use of preliminary data and extrapolation methodologies for these most recent years. While there is a growing number of countries today on a sustained emission reduction trajectory, our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. We conclude by highlighting that tracking progress in climate policy requires substantial investments in independent GHG emission accounting and monitoring as well as the available 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.5053056.

Analysis of CO₂ spatio-temporal variations in China using a weather–biosphere online coupled model

The dynamics of atmospheric CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from the terrestrial flux and the influence of atmospheric mixing. In this study we apply the WRF-Chem model configured with the Vegetation Photosynthesis and Respiration Model (VPRM) option for biomass fluxes in China to characterize the dynamics of CO2 in the atmosphere. The online coupled WRF-Chem model is able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. We apply WRF-Chem for a multi-year simulation (2016–2018) with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the spatio-temporal variations of CO2 fluxes and concentrations in China. We find that the spatial distribution of CO2 was dominated by anthropogenic emissions, while its seasonality (with maxima in April 15 ppmv higher than minima in August) was dominated by the terrestrial flux and background CO2. Observations and simulations revealed a consistent increasing trend in column-averaged CO2 (XCO2) of 2.46 ppmv (0.6 % yr−1) resulting from anthropogenic emission growth and biosphere uptake. WRF-Chem successfully reproduced ground-based measurements of surface CO2 concentration with a mean bias of −0.79 ppmv and satellite-derived XCO2 with a mean bias of 0.76 ppmv. The model-simulated seasonality was also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and satellite data, respectively. Tower observations from a background site at Lin'an (30.30∘ N, 119.75∘ E) revealed a strong correlation (−0.98) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

The Dietary Supplemental Effect of Nitroethanol in Comparison with Monensin on Methane Emission, Growth Performance and Carcass Characteristics in Female Lambs

This study was conducted to evaluate the dietary supplemental effects of 2-nitroethanol (NEOH) in comparison with monensin on methane (CH4) emission, growth performance and carcass characteristics in female lambs. Sixty female, small-tailed Chinese Han lambs (3.5 ± 0.3 month) were randomly allotted into three dietary treatment groups: (1) Control group, a basal control diet, (2) monensin group, the basal diet added with 40 mg/kg monensin, (3) NEOH group, the basal diet added with 277 mg/kg nitroethanol, and the feedlotting trial lasted for 70 days. Although dietary addition of monensin and NEOH did not affect nutrient digestibility of lambs, both monensin and NEOH decreased the calculated CH4 production (12.7% vs. 17.4% decrease; p < 0.01). In addition, the CH4 production represents less dietary energy loss in the monensin and NEOH group than in the control, indicating that monensin and NEOH are potent CH4 inhibitors that can reduce dietary energy loss. Dietary addition of monensin and NEOH decreased dry matter intake (p < 0.01); however, they increased the ADG of female lambs (p < 0.01). As a result, both monensin and NEOH increased feed conversion efficiency of the feedlotting lambs (p < 0.01), suggesting that feed energy saved from CH4 production promoted the feed efficiency and ADG in the present study. Except for the fact that NEOH addition increased the net muscle percentage to carcass weight (p = 0.03), neither monensin nor NEOH had a significant influence on carcass characteristics of female lambs (p > 0.05). From an economic point of view, NEOH and monensin caused a reduction in feed consumption costs, therefore resulting in a higher net revenue and economic efficiency than the control. In summary, dietary supplementation of NEOH in comparison with monensin presented a more promoting effect on energy utilization in female lambs by inhibiting rumen methanogenesis more efficiently, and NEOH improved the net revenue and economic efficiency more significantly than monensin.

A Literature Evaluation of Systemic Challenges Affecting the European Maritime Energy Transition

Energy transition is affecting the European maritime sector at an increasing rate. New technologies and regulations are being introduced with increasing speed. The ability to adapt to these changes is crucial for the economic success of the maritime sector. However, the sector is challenged by inertia due to its global nature and long-life assets (e.g., vessels). These developments result in a globally projected greenhouse gas emission growth rather than a reduction towards 2050. The sector can be considered essential to economic prosperity, but its innovation system should align with global sustainability trends. This article aims to structure and evaluate the maritime sector’s systemic challenges by conducting an extensive systematic review of (sustainable) maritime innovation literature. These findings are structured and discussed via four key activities that support the transition process: developing strategy and policy, creating legitimacy, mobilizing resources, and developing and disseminating knowledge.

Analysis of CO₂ spatiotemporal variations in China using tower data and a weather-biosphere-online-coupled model, WRF-VPRM

Dynamics of CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from terrestrial flux and the influence of atmospheric mixing. In this study we apply the Weather Research and Forecasting model coupled with Vegetation Photosynthesis and Respiration Model (WRF-VPRM) in China to characterize CO2 dynamics with tower data collected at a background site Lin’an (30.30° N, 119.75° E). The online coupled weather-biosphere WRF-VPRM simulations are able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. Simulations are conducted for three years (2016–2018) with fine grid resolution (20 km) to detail the spatiotemporal variations of CO2 fluxes and concentrations. This is the first attempt to apply the weather-biosphere model for a multi-year simulation with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the dynamics of CO2 in China. We find that the spatial distribution of CO2 is determined by anthropogenic emissions, while its seasonality (with maximum concentrations in April 15 ppmv higher than minimums in August) is dominated by terrestrial flux and background CO2. Observations and simulations reveal a consistent increasing trend in column-averaged CO2 (XCO2) of 0.6 %/yr resulting from anthropogenic emission growth and biosphere uptake. WRF-VPRM successfully reproduces ground-based measurements of surface CO2 concentration with mean bias of −0.79 ppmv (−0.20 %) and satellite derived XCO2 with mean bias of 0.76 ppmv (0.19 %). The model-simulated seasonality is also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and Orbiting Carbon Observatory-2 (OCO-2) satellite data, respectively. However, evaluation against Lin'an tower data reveals uncertainty within the model for simulating the intensity and diurnal variation of terrestrial flux, which contributes to overestimation by ~5.35 ppmv (1.26 %). Lin'an tower observations also reveal a strong correlation (−0.85) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

Evaluating the Carbon Emissions Efficiency of the Logistics Industry Based on a Super-SBM Model and the Malmquist Index from a Strong Transportation Strategy Perspective in China

Carbon emissions from the logistics industry have been rising year after year. Correct handling of the relationship between economic development and environmental protection is of great significance to the implementation of green logistics, which is an important component of China’s strategy for strong transportation. This paper focuses on the evaluation of the carbon emissions efficiency of logistics industry from a new strong transportation strategy perspective. A super-efficiency slack-based measurement (Super-SBM) model and Malmquist index are combined to evaluate the static and dynamic carbon emissions efficiency of the logistics industry. The results indicate that compared with the SBM model, the Super-SBM model can more effectively measure the carbon emissions efficiency of the logistics industry. Pilot regions for the strong transportation strategy were divided into two categories, namely regions with slow carbon emission growth rates but high efficiency, and regions with high carbon emission growth rates but low efficiency. Some policy recommendations from the strong transportation strategy perspective were proposed to improve the carbon emissions efficiency of the logistics industry, especially for the second category of pilot regions. This study is expected to provide a basis for decision-making for efficient emissions reduction measures and policies, and to encourage the pilot regions to take the lead in achieving the goal of China’s strategy for transportation.