Special collections for applying artificial intelligence techniques to encourage economic growth and maintain sustainable societies.

Efficient resource use requires substantial evaluation and re-evaluation of production processes. Since not all production details can be properly and timely monitored and adjusted, improvement of resource allocation has long been a critical issue in commodity production, technique selection, and sustainable development. The progress of artificial intelligence (AI) offers a possibility. For example, with deep learning techniques and extensive data analysis, most previously unincorporated or unknown information associated with a firm's activities can be appropriately reflected and calculated. Once the firms take the report, a more efficient and economically friendly production strategy could be made. The central theme of this special collection is to invite studies on how the design and application of AI benefit not only the fields of computer science and information engineering but also the interdisciplinary fields, including renewable energy development, environmental protection, and economic analysis. Fourteen papers are published in this special collection.

Prospects of Taiwan's solar energy development and policy formulation.

Taiwan lacks energy stock and has been paying great attention to developing renewable energy to improve energy security and sustain economic growth. Solar energy is attractive to Taiwan's government as the recorded radiation is substantial, and a significant amount of fallow land is available for panel installation. This study investigates the potential solar energy production from Crystalline silicon (c-Si) and cadmium Telluride thin-film (CdTe) cell systems, estimates each system's capital requirement, and compares the economic and environmental benefits to explore effective investment strategy. The results show that, on average, the c-Si module could produce 10,644 GWh per year while the CdTe mode would yield a total electricity of about 9365 GWh. The useful life also plays an essential role in the investment requirement. With a 30-year useful life, the systems can reduce the annualized installation and maintenance cost to about NT$3.16 billion. In terms of offset efficiency, every MWh produced would result in at least 13.63-15.49 metric ton of carbon emission offset, and the offset value per GWh can be up to NT$5.77 million, which provide attractive economic incentives to energy suppliers. We point out that the acquisition of low-cost financing sources such as green bonds, as well as the improvement of current emission trading systems (ETS), would greatly benefit solar energy development.

Impact of new talent settlement policy on housing prices: Evidence from 70 large and medium-sized Chinese cities.

Since 2017, Chinese cities have set off a wave of talent migration, with major cities joining the talent war and issuing new talent settlement policies that might stimulate the real estate market through the inflow and outflow of human capital. However, the effects of new talent settlement policies on housing prices have not been extensively studied. This study used a difference-in-differences model to examine the causal effects of new talent settlement policies on housing prices in China based on data from 70 large and medium-sized cities. The results showed that new talent settlement policies had positive effects on housing prices, and the effects revealed pronounced regional heterogeneity: they were more significant in the eastern region, first-tier, and new first-tier cities, and varied across major migration zones. Further, the varying policy tools in the new talent settlement policies had disparate effects on housing prices. Thus, we recommend that new talent settlement policies must be coordinated with the goals of real estate regulation and reasonable regional standards, and that the policy tools should be tailored according to the actual conditions of cities.

Machine learning in the evaluation and prediction models of biochar application: A review.

This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

The development of input-monitoring system on biofuel economics and social welfare analysis.

Biofuel production relies on stable supply of biomass which would be significantly influenced by climate-induced impacts. Since the actual agricultural outputs are relatively unpredictable in the face of uncertain environmental conditions and can only be realized in the harvest season, providing useful information regarding the stability of biomass supply to the downstream biofuel industry is crucial. This study firstly illustrates a theoretical framework to explore the resultant market equilibrium and optimal conditions of agricultural and bioenergy production in the face of highly uncertain environmental risks and then employs a two-stage stochastic programming model to investigate the optimal biofuel development and associated economic and environmental effects. The results show that total welfare may not always increase because the loss of other agricultural commodities induced by climate impacts may be greater than the gains received by biofuel production and emission reduction. This study provides insights into the area where artificial intelligence monitoring system can be implemented to analyze the input data associated with agricultural activities and help the biofuel industry to improve its production possibilities.

An environmental and economic evaluation of pyrolysis for energy generation in Taiwan with endogenous land greenhouse gases emissions.

Taiwan suffers from energy insecurity and the threat of potential damage from global climate changes. Finding ways to alleviate these forces is the key to Taiwan's future social and economic development. This study examines the economic and environmental impacts when ethanol, conventional electricity and pyrolysis-based electricity are available alternatives. Biochar, as one of the most important by-product from pyrolysis, has the potential to provide significant environmental benefits. Therefore, alternative uses of biochar are also examined in this study. In addition, because planting energy crops would change the current land use pattern, resulting in significant land greenhouse gases (GHG) emissions, this important factor is also incorporated. Results show that bioenergy production can satisfy part of Taiwan's energy demand, but net GHG emissions offset declines if ethanol is chosen. Moreover, at high GHG price conventional electricity and ethanol will be driven out and pyrolysis will be a dominant technology. Fast pyrolysis dominates when ethanol and GHG prices are low, but slow pyrolysis is dominant at high GHG price, especially when land GHG emissions are endogenously incorporated. The results indicate that when land GHG emission is incorporated, up to 3.8 billion kWh electricity can be produced from fast pyrolysis, while up to 2.2 million tons of CO2 equivalent can be offset if slow pyrolysis is applied.

Simulation of regionally ecological land based on a cellular automation model: a case study of Beijing, China.

Ecological land is like the "liver" of a city and is very useful to public health. Ecological land change is a spatially dynamic non-linear process under the interaction between natural and anthropogenic factors at different scales. In this study, by setting up natural development scenario, object orientation scenario and ecosystem priority scenario, a Cellular Automation (CA) model has been established to simulate the evolution pattern of ecological land in Beijing in the year 2020. Under the natural development scenario, most of ecological land will be replaced by construction land and crop land. But under the scenarios of object orientation and ecosystem priority, the ecological land area will increase, especially under the scenario of ecosystem priority. When considering the factors such as total area of ecological land, loss of key ecological land and spatial patterns of land use, the scenarios from priority to inferiority are ecosystem priority, object orientation and natural development, so future land management policies in Beijing should be focused on conversion of cropland to forest, wetland protection and prohibition of exploitation of natural protection zones, water source areas and forest parks to maintain the safety of the regional ecosystem.