Energy Consumption, Carbon Emissions and Global Warming Potential of Wolfberry Production in Jingtai Oasis, Gansu Province, China

During the last decade, China's agro-food production has increased rapidly and been accompanied by the challenge of increasing greenhouse gas (GHG) emissions and other environmental pollutants from fertilizers, pesticides, and intensive energy use. Understanding the energy use and environmental impacts of crop production will help identify environmentally damaging hotspots of agro-production, allowing environmental impacts to be assessed and crop management strategies optimized. Conventional farming has been widely employed in wolfberry (Lycium barbarum) cultivation in China, which is an important cash tree crop not only for the rural economy but also from an ecological standpoint. Energy use and global warming potential (GWP) were investigated in a wolfberry production system in the Yellow River irrigated Jingtai region of Gansu. In total, 52 household farms were randomly selected to conduct the investigation using questionnaires. Total energy input and output were 321,800.73 and 166,888.80 MJ ha−1, respectively, in the production system. The highest share of energy inputs was found to be electricity consumption for lifting irrigation water, accounting for 68.52%, followed by chemical fertilizer application (11.37%). Energy use efficiency was 0.52 when considering both fruit and pruned wood. Nonrenewable energy use (88.52%) was far larger than the renewable energy input. The share of GWP of different inputs were 64.52% electricity, 27.72% nitrogen (N) fertilizer, 5.07% phosphate, 2.32% diesel, and 0.37% potassium, respectively. The highest share was related to electricity consumption for irrigation, followed by N fertilizer use. Total GWP in the wolfberry planting system was 26,018.64 kg CO2 eq ha−1 and the share of CO2, N2O, and CH4 were 99.47%, 0.48%, and negligible respectively with CO2 being dominant. Pathways for reducing energy use and GHG emission mitigation include: conversion to low carbon farming to establish a sustainable and cleaner production system with options of raising water use efficiency by adopting a seasonal gradient water pricing system and advanced irrigation techniques; reducing synthetic fertilizer use; and policy support: smallholder farmland transfer (concentration) for scale production, credit (small- and low-interest credit) and tax breaks

Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

[[abstract]]Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon’s path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.[[notice]]補正完

Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems

This study was conducted in order to determine energy consumption, model and analyze the input–output, energy efficiencies and GHG emissions for watermelon production using artificial neural networks (ANNs) in the Guilan province of Iran, based on three different farm sizes. For this purpose, the initial data was collected from 120 watermelon producers in Langroud and Chaf region, two small cities in the Guilan province. The results indicated that total average energy input for watermelon production was 40228.98 MJ ha–1. Also, chemical fertilizers (with 76.49%) were the highest energy inputs for watermelon production. Moreover, the share of non-renewable energy (with 96.24%) was more than renewable energy (with 3.76%) in watermelon production. The rate of energy use efficiency, energy productivity and net energy was calculated as 1.29, 0.68 kg MJ−1 and 11733.64 MJ ha−1, respectively. With respect to GHG analysis, the average of total GHG emissions was calculated about 1015 kgCO2eq. ha−1. The results illustrated that share of nitrogen (with 54.23%) was the highest in GHG emissions for watermelon production, followed by diesel fuel (with 16.73%) and electricity (with 15.45%). In this study, Levenberg–Marquardt learning Algorithm was used for training ANNs based on data collected from watermelon producers. The ANN model with 11–10–2 structure was the best one for predicting the watermelon yield and GHG emissions. In the best topology, the coefficient of determination (R2) was calculated as 0.969 and 0.995 for yield and GHG emissions of watermelon production, respectively. Furthermore, the results of sensitivity analysis revealed that the seed and human labor had the highest sensitivity in modeling of watermelon yield and GHG emissions, respectively

Mini review on life cycle assessment of chemical recycling for polyethylene terephthalate packaging:A background for UPLIFT project

Polyethylene terephthalate (PET) is one of the most widely used materials for packaging due to its excellent properties such as high strength, transparency, and recyclability. However, PET waste poses a significant environmental problem, as it can take hundreds of years to decompose and can cause pollution and harm to wildlife. To address this issue, several recycling methods have been developed, including mechanical recycling, thermal recycling, and chemical recycling. Among these methods, chemical recycling is a promising technology as it can convert PET waste into valuable chemicals and monomers that can be used to produce new PET or other materials. Life cycle assessment (LCA) is a well-established method used to evaluate the environmental impacts of a product or process throughout its entire life cycle. LCA can provide insights into the potential environmental benefits and drawbacks of different recycling methods for PET packaging. The present study begins by providing an overview of chemical recycling methods, followed by an explanation of the principles of LCA. The findings indicate that among the various impact assessment methods, six methods are most commonly used, with researchers predominantly focusing on global warming potential (GWP). The range of GWP values observed falls between -4.5 and 4.12 kg CO2-eq. Noteworthy challenges identified in the reviewed studies encompass issues related to data availability and reliability, boundary definition, allocation methods, variability in recycling technologies, and impact assessment methods. To address these challenges, suggested improvements entail enhancing data collection and verification processes, clearly defining study boundaries, considering appropriate allocation methods, investigating the efficiency and environmental performance of diverse recycling technologies, and selecting impact assessment methods that comprehensively capture environmental impacts. Lastly, the UPLIFT project endeavors to develop sustainable PET packaging through chemical recycling, aiming to bridge the research gap in comprehensive LCA studies specifically focused on chemical recycling for PET packaging