Evaluation of Agricultural Climatic Resource Utilization During Spring Maize Cultivation in Northeast China Under Climate Change

Agricultural climatic resources （such as light,temperature,and water） are environmental factors that affect crop productivity.Predicting the effects of climate change on agricultural climatic resource utilization can provide a theoretical basis for adapting agricultural practices and distributions of agricultural production.This study investigates these effects under the IPCC （Intergovernmental Panel on Climate Change） scenario A1B using daily data from the high-resolution RegCM3 （0.25° ×0.25°） during 1951-2100.Model outputs are adjusted using corrections derived from daily observational data taken at 101 meteorological stations in Northeast China between 1971 and 2000.Agricultural climatic suitability theory is used to assess demand for agricultural climatic resources in Northeast China during the cultivation of spring maize.Three indices,i.e.,an average resource suitability index （Isr）,an average efficacy suitability index （Ise）,and an average resource utilization index （K）,are defined to quantitatively evaluate the effects of climate change on climatic resource utilization between 1951 and 2100.These indices change significantly in both temporal and spatial dimensions in Northeast China under global warming.All three indices are projected to decrease in Liaoning Province from 1951 to 2100,with particularly sharp declines in Isr,Ise,and K after 2030,2021,and 2011,respectively.In Jilin and Heilongjiang provinces,Isr is projected to increase slightly after 2011,while Ise increases slightly and K decreases slightly after 2030.The spatial maxima of all three indices are projected to shift northeastward.Overall,warming of the climate in Northeast China is expected to negatively impact spring maize production,especially in Liaoning Province.Spring maize cultivation will likely need to shift northward and expand eastward to make efficient use of future agricultural climatic resources.

Climate bonds toward achieving net zero emissions and carbon neutrality:Evidence from machine learning technique

The Conference of the Parties(COP26 and 27)placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality.However,studies on the implementation of this policy proposition are limited.To address this gap in the literature,this study employs machine learning techniques,specifically natural language processing(NLP),to examine 77 climate bond(CB)policies from 32 countries within the context of climate financing.The findings indicate that“sustainability”and“carbon emissions control”are the most outlined policy objectives in these CB policies.Additionally,the study highlights that most CB funds are invested toward energy projects(i.e.,renewable,clean,and efficient initiatives).However,there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019.This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry,potentially leading to the greenwashing of climate funds.Furthermore,policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change,which may negatively influence climate actions.Thus,the findings highlight that the effective implementation of CB policies depends on policy goals,objectives,and sentiments.Finally,this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.