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Considers an application of adaptive control policy to dynamic input‐output systems of Japanese large‐scale industrial (primary, secondary and tertiary) sectors by neural networks. The adaptive control policy has three steps. The first is to obtain the optimal control policy such that the minimization of the weighted sum of the squared deviation between the actual targets and the desired subject to econometric models is achieved. The second is to determine the optimal outputs for each industrial sector through dynamic input‐output system under the optimal control policies. The third is to obtain the network outputs by neural network algorithm through the controlled output equations derived from DIO system. We consider what affects the outputs if the optimal control policy was adopted, and how the change of industrial structure has occurred after the bubble burst in 1990s in Japan during 1985 through 1993, and we predict the future of the industries up to 2010 by using DIO linked to the final demand econometric models of the Japanese industrial sectors by simulation.

The purpose of this paper is to explore the empirical relationship between the share of immigrants and the price elasticity of import demand.

We estimate the import demand function including the interaction term of the share of immigrants and relative import price, using panel data of 76 countries/areas.

The coefficient of the interaction term is significantly positive, that is, a higher share of immigrants weakens the negative effect of the relative import price on import demand. Our findings reveal the negative relationship between the share of immigrants and the price elasticity of import demand.

The share of immigrants is increasing in the present era of globalization, and it is possible that the role of exchange rate as the price adjustment mechanism in international trade become lower in the future.

This research considers different price elasticities for import goods by immigrants and natives.

The purpose of this study is to analyze the Environmental Kuznets Curve (EKC) hypothesis within the methane (CH₄) emission–economic growth nexus among the six Association of Southeast Asian Nations (ASEAN) countries from 1985 to 2012.

The study employs dynamic panel data estimation approaches such as mean group (MG) and pooled MG (PMG) techniques.

The findings reveal that the EKC hypothesis for the CH₄ emission in these economies proves to be valid. In other words, economic growth causes CH₄ emissions to decrease. Nevertheless, energy consumption is deteriorating the environment by enhancing CH₄ emissions in these countries.

The ASEAN region has experienced substantial economic growth over the previous few decades. Nevertheless, pollution has also increased manifolds in this region. Methane is a more potent greenhouse gas (GHG) as compared to carbon dioxide (CO₂) and a major source of socio-economic issues in the ASEAN region. This study is the first in the existing literature on the EKC hypothesis examining the role of economic growth on CH₄ emissions in the selected ASEAN countries. The outcomes of this study could be really beneficial for the policymakers in this region regarding sustainability and economic development.

From a forced renunciation in 1946 of war as a tool of international relations, Japan progressed during the next 58 years – first pushed by a former enemy, then entrained by world events and a renewed emergence of nationalism at home – to de facto repudiation of a position of studied neutrality. Armed Japanese troops are today once again present in a theatre of war.

Human beings spend their daily lives within the range of the atmospheric boundary layer, where airflow is affected by friction from Earth's surface. The airflow in this area is generally called wind. Strong wind occasionally causes severe damage to infrastructures and people because of its aerodynamic effects, but even weak and moderate winds can have serious environmental impacts on human society such as those seen with air-pollution problems and thermal effects.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.