We explore the opportunity to apply Artificial Intelligence (AI) and T-Shaped Teams in risk management post the COVID-19 crisis and beyond. First, the study describes the three stages of AI adoption and the interest in AI before and during the COVID-19 pandemic. Second, the importance of AI in risk management is assessed based on the analysis of the performance of AI-implemented firms. Third, the roles of workforce, especially T-Shaped teams, in adopting AI at the firm level are highlighted. The new Technology, Innovation, and In-practice functions of the T-shaped teams are discussed along with the opportunities and challenges when building a T-shaped team and adopting AI in enterprises. Next, a guide for building T-Shaped Teams for risk management using AI is developed with the flexibility to implement in a wide range of corporates. The study concludes with future direction on risk management strategies to respond to new risks and make better business decisions to address shocks in the economy.

Do rising oil prices affect agricultural trade differentlyfrom falling oil prices? We answer this question usingdata on New Zealand, a net importer of oil and a netexporter of agricultural commodities. We consider adisaggregated approach, analysing exports and importsof five key commodity classes; nonlinear autoregressivelag models are employed to conduct the analysis. Wefind considerable evidence suggesting asymmetries inthe effects of oil price shocks on agricultural trade inthe long and the short run. Furthermore, in the longrun, agricultural exports and imports appear to belargely insensitive to foreign and domestic real income,respectively; there is limited evidence for imports andexports being associated with the real effectiveexchange rate. In the short run, however, income andexchange rates are associated with imports and exports.

This study analyses the impact of clean energy use on rural residents’ subjective health outcomes (self-reported health status, health change, and discomfort) and objective health outcomes (bronchitis, asthma, medical expenditure, and fitness expenditure). Using an inverse probability-weighted regression adjustment estimator and the 2018 China Family Panel Studies data, we address the selection bias associated with clean energy use and estimate the treatment effects. The empirical results show that farmers using clean energy (liquid gas, natural gas, methane, solar energy, or electricity) as the primary cooking fuel report improved health, a lower probability of physical discomfort, and higher fitness expenditures than non-users. Clean energy use does not significantly affect self-reported health, the probability of having bronchitis and asthma, or medical expenditures. The health effects of clean energy use on men differ from those on women; they also vary across different economic strata. We also find that farmers’ decisions to use clean energy are positively associated with their educational level, household income, whether they rent farmland, and their happiness levels but are negatively related to their age, family size, whether they own real estate, and the ratio of elders in their household.

We use dynamic time warping, a non-parametric pattern recognition method, to study interlinkages between major energy and agricultural commodity prices. Cluster analysis is conducted to group commodity prices based on their behavioral likeness by maximizing the differences between groups while minimizing the differences within groups. Two clusters emerge: one comprises the prices of crude oil and six major agricultural commodities, whereas the other contains coal and natural gas prices. Regarding lead-lag associations, oil prices generally lag crop prices; however, there are periods during which the former lead the latter. Furthermore, the duration with which oil prices lead or lag crop prices changes frequently.

The New Zealand External Reporting Board (XRB) is-sued for public comment a standard on Assurance Engagements over GHG Emissions Disclosure. The Auditing and Assurance Standards Committee of AFAANZ prepared a submission, based on the findings reported in published research, responding to a number of the questions asked by the XRB. This technical note presents the formal submission made to the XRB.

There is an opportunity and an urgent need for transformational change of the current farming systems in Pacific Island Countries (PIC) to increase food and nutritional security, to be better adapted to climate change and to be less emissive of greenhouse gases. Substantial global evidence indicates that Conservation Agriculture and Sustainable Intensification (CASI) systems are better adapted, but their effectiveness in PIC is yet to be fully evaluated. The overall aim of this Small Research Activity (SRA) was to undertake a targeted assessment to explore the opportunities for implementing CASI in smallholder farming systems as an adaptive and potentially transformational climate change response in PIC. This SRA was a Proof of Concept which identified and evaluated the research, technological, social and policy interventions required for future implementation and scaling of these more regenerative agrifood systems. It also identified key research and development sites for future work to compare the performance of CASI practices with the current commonly used systems.

We use structural vector autoregressions to analyse the dynamic effects of shocks to natural gas and nitrogenous fertiliser prices on three major cereal crops: maize, rice and wheat. We find that the response of cereal prices to natu-ral gas and fertiliser price shocks has been relatively small, instantaneous and transitory. These findings suggest that crop prices may change rapidly in response to energy and fertiliser prices, even when there are no shifts in the under-lying fundamentals in crop markets. Furthermore, because the effects of the shocks dissipate rather quickly, short-term measures to address swings in food prices may suffice.

The uptake of actions to mitigate climate change at the household level might crowd out subsequent policy at the national level, which is problematic because national policy often has a larger mitigation potential than individual household measures. This study analyses crowding out between the uptake of low-cost actions and the support for national climate change policy in the agricultural sector. In the experimental set-up, survey respondents were primed to think about the implementation of low-cost mitigation practices and subsequently asked to express support for national mitigation policy. The results show a crowding-out effect between individual mitigation measures and support for national policy. Individuals with high levels of worry show a stronger crowding out effect. This study contributes to building understanding of when and why crowding out occurs in order to help frame and communicate future climate change policy.

This paper develops a new process for identifying the lowest cost package of energy efficiency measures (EEM) and hybrid energy system configuration for off-grid homes. Hybrid energy systems, which combine two or more types of energy technologies, often require significant capital expenditure, however,

the cost can be reduced by applying EEM to the house to decrease energy demand. The method proposed here, termed Combined Optimization Process (COP), was tested on an off-grid hypothetical case and incorporates an iterative assessment of a building energy and efficiency optimization tool (BEopt) and a hybrid system optimization tool (HOMER). The COP results were compared with the base case where no efficiency measures were applied, and also with a standard process, which involved a selection of bestpractice efficiency measures. The COP method yielded net present cost savings of 10% less than the base case, and 5% less than the standard process. The COP method developed in this paper is applicable for existing houses converting to off-grid status as well as for the design stage of off-grid houses.

Agriculture accounts for 48% of New Zealand’s gross greenhouse gas (GHG) emissions with nearly 75% of the emissions coming from methane (CH4) production from ruminant animals (enteric-CH4) (Ministry for the Environment 2020). Changes to alternative low biogenic greenhouse gas emission (BGE) land uses is an option for reducing national GHG and meeting national reduction targets for overall GHG and CH4. However, large land use change will have implications for New Zealand’s economy. To address this, replacing profitable livestock with alternative profitable land uses would potentially overcome this concern.

This study evaluates energy efficiency of pastoral (PDFs) and barn (BDFs) dairy farming systems in New Zealand through application of data envelopment analysis (DEA) approach. Two models constant return to scale (CCR) and variable return to scale (BCC) of DEA were employed for determining the technical (TE), pure technical (PTE) and scale (SE) efficiencies of New Zealand pastoral and barn dairy systems. Further, benchmarking was also performed to separate efficient and inefficient dairy farms and energy saving potential was identified for both dairy systems based upon their optimal energy consumption. For this study, the energy inputs data were taken from 50 dairy farms (including PDFs and BDFs) across Canterbury, New Zealand. The results indicated that the average technical, pure technical and scale efficiencies of pastoral (PDFs) dairy systems were 0.84, 0.90, 0.93 and for barn (BDFs) systems were 0.78, 0.84, 0.92, respectively, showing that energy efficiency is slightly better in PDFs system than the BDFs. From the total number of dairy farms 40% and 48% were efficient based on the constant return to scale and variable return to scale models, respectively. Further, the energy saving potential for PDFs and BDFs dairy systems through optimal energy consumption were identified as 23% and 35%, respectively. Thus, energy auditing, use of renewable energy and precision agricultural technology were recommended for energy efficiency improvement in both dairy systems.

This study analyses the impact of clean energy use on rural residents’ subjective health outcomes (self-reported health status, health change, and discomfort) and objective health outcomes (bronchitis, asthma, medical expenditure, and fitness expenditure). Using an inverse probability-weighted regression adjustment estimator and the 2018 China Family Panel Studies data, we address the selection bias associated with clean energy use and estimate the treatment effects. The empirical results show that farmers using clean energy (liquid gas, natural gas, methane, solar energy, or electricity) as the primary cooking fuel report improved health, a lower probability of physical discomfort, and higher fitness expenditures than non-users. Clean energy use does not significantly affect self-reported health, the probability of having bronchitis and asthma, or medical expenditures. The health effects of clean energy use on men differ from those on women; they also vary across different economic strata. We also find that farmers’ decisions to use clean energy are positively associated with their educational level, household income, whether they rent farmland, and their happiness levels but are negatively related to their age, family size, whether they own real estate, and the ratio of elders in their household.