Multi‐objective particle swarm optimisation of complex product change plan considering service performance

Design change is an inevitable part of the product development process.This study proposes an improved binary multi‐objective PSO algorithm guided by problem char-acteristics(P‐BMOPSO)to solve the optimisation problem of complex product change plan considering service performance.Firstly,a complex product multi‐layer network with service performance is established for the first time to reveal the impact of change effect propagation on the product service performance.Secondly,the concept of service performance impact(SPI)is defined by decoupling the impact of strongly associated nodes on the service performance in the process of change affect propagation.Then,a triple‐objective selection model of change nodes is established,which includes the three indicators:SPI degree,change cost,and change time.Furthermore,an integer multi‐objective particle swarm optimisation algorithm guided by problem characteristics is developed to solve the model above.Experimental results on the design change problem of a certain type of Skyworth TV verify the effectiveness of the established optimisation model and the proposed P‐BMOPSO algorithm.

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.

Systematic Synthesis of Impacts of Climate Change on China's Crop Production System

Global climate change is real and already taking place. The most recent Fifth Assessment Report of the Intergov- ernmental Panel on Climate Change （IPCC AR5） stated that global land and ocean surface temperature increased by 0.85℃ over the period of 1880 to 2012 （IPCC Climate Change 2013）. China is among the most affected countries by global climate change.

The Potential Scenarios of the Impacts of Climate Change on Egyptian Resources and Agricultural Plant Production

The emissions of greenhouse gasses in Egypt are about 0.58% of the total emissions of the world in the year 2015, although Egypt is one of the countries most affected by the impacts of climate change. By assessment and analysis of the expected economic impacts of climate change by the year 2030, the Egyptian cultivated area will be reduced to about 0.949 million acres, equal to about 8.22% of the Egyptian cultivated area compared with the case of no sinking part of the Delta land, thus reducing crop area in Egypt to about 1.406 million acres, approximately to about 6.25% of crop area compared with the case of no sinking part of the Delta land, in addition to surplus in the Egyptian balance water to about 2.48 billion m3. In this case value of the Egyptian agriculture production will decrease by about 6.19 billion dollars, equal to about 6.19% compared with presumably no sinking of the Delta land. In the case of sinking 15% of Delta lands, with the change of the productivity and water consumption of most crops, the result will be a reduction in the cultivated area to about 0.94 million acres. In addition to decreasing the Egyptian crop area to about 1.39 million acres, with a deficit in the Egyptian balance water to about 4.74 billion m3 compared to the case of no sinking part of the Delta land, the cultivated area will decrease to about 8.17%, and the crop area will decrease 6.18%. Also, the value of the Egyptian agriculture production will decrease by about 12.51%. While compared to sinking part of the Delta land to about 15% of the total Delta area without the other impacts of climate change, the cultivated area will increase by about 0.06%;the crop area will increase by about 0.08%;also, the value of the Egyptian agriculture production will decrease by about 5.57%.

The Impact of the Establishment of National High-tech Zones on Total Factor Productivity of Chinese Enterprises

The National High-tech Zone(NHTZs)is an important strategic platform for cultivating high-tech industries and realizing high-quality economic development in China.Based on the combined data from 2006 to 2014 of the industrial enterprise database,the customs database,and the China Development Zones Audit and Announcement Catalogue(abbreviated asthe Catalogue),this paper systematically investigates the influence of the construction of NHTZs on enterprise’s total factor productivity(TFP).Results show that NHTZs have a positive impact on the TFP of enterprises in the zone,and this conclusion is still valid after considering endogeneity problems.Furthermore,the above productivity effects of NHTZs are heterogeneous in terms of enterprise ownership,external environment and establishment time,and NHTZs have greater stimulation effects on enterprise productivity after comparing with other types of functional zones.An investigation of the specific mechanisms at play shows that NHTZs promote the TFP of enterprises in the zone through the release of preferential policies,strengthening the“technology spillover effects”of imported intermediate goods,enhancing enterprise’s innovation ability and attracting talent.In addition,based on the decomposition of industry productivity,this paper also investigates the impact of NHTZs on changes in industry productivity and finds that NHTZs promote the overall productivity of specific industries mainly by stimulating the productivity improvement of incumbent enterprises and expanding the market share of high-productivity enterprises.Moreover,the preferential policies of NHTZs do not significantly stimulate high-productivity enterprises to enter the zones,nor do they cause low-productivity enterprises to exit.This research is helpful in objectively evaluating the economic effects of the NHTZs in China and in providing a theoretical basis for its further adjustment.

The higher grain production, the more social deprivation? A case study of Henan province in traditional agricultural areas of China

This paper examines the spatial-temporal dynamics of inequality from the perspective of social deprivation in the context of continuous growth of grain production using national census data from 2000 and 2010 for Henan province,China.Our analytical results show an overall level of social deprivation that is high and a widening social deprivation gap at the county level.The social deprivation shows great spatiotemporal heterogeneity,the higher deprived counties are mainly distributed in central and eastern Henan province,while counties with lower social deprivation are clustered in northwestern regions,formed a belt-shape spatial differential between the south(higher deprived counties clustered)and northwest(low deprived counties clustered)Henan in 2000,and changed toward a core-periphery semi annular spatial structure in 2010,the low deprived counties surrounded by high deprived counties.The social deprivation shows a"west low and east high"spatial pattern,similar to the grain production in Henan province.Socioeconomic development level was low in most counties in Yudong plain despite of keeping the continuous increase of grain production,meaning the higher grain output did not result in the more social development expected in Chinese traditional agricultural areas,and the regional disparity is expected to increase in the near future if development conditions are not changed,which could inspire more targeted rural governance options.

Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.

Changes in Grain Production and the Optimal Spatial Allocation of Water Resources in China

Changes in grain production are decomposed and compared among nine major Chinese river basins for the sake of optimal water allocation. The results show that water-deficient northern China, especially the Songliao River Basin and Huai River Basin, contributed the greatest share of the total grain increment from 1995 to 2010. The Songliao River Basin achieved increased grain output largely by expanding multiple cropping, while the Huai River Basin achieved it mainly by improving the yield per unit area. With increased reliance on expanding irrigation and multi-cropping, most northern basins have high levels of agricultural water consumption, despite the rising share of corn, a lower water intensive crop. In contrast, over the same period the warm and humid south, traditionally a major rice-growing area, mostly experienced a sharp decline in rice cropping area and the Southeast Rivers Basin even reduced multiple cropping indexes, contributing to decreased agricultural water consumption. Implications of our findings and the need for tackling the imbalance of agricultural water use in grain production are discussed.

Historical Transformation of China＇s Agriculture＂ Productivity Changes and Other Key Features

According to an analysis of sampled data from the National Bureau of Statistics for 70,000 peasant households, agricultural labor productivity has been increasing at a faster rate than land productivity since 2003. Labor productivity has, in fact, experienced long-term stagnation, The data also reveals the heterogenization of small farmers, farm machinery replacing manual labor as an agricultural input, the expansion of the scale of land management, the development of the rural land leasing market and the diversification of the agricultural management entities. Review of the historical transformation of agriculture helps to recognize the declining importance of agricultural land, the direction of agricultural technological changes, the path and the disposition of the changes to the agricultural system, and the adjustment of China＇s rural policies.

Impact of Climate Change on Maize Potential Productivity and the Potential Productivity Gap in Southwest China

The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China（SWC） are investigated in this paper.We analyze the impact of climate change on the photosynthetic,light-temperature,and climatic potential productivity of maize and their gaps in SWC,by using a crop growth dynamics statistical method.During the maize growing season from 1961 to 2010,minimum temperature increased by 0.20℃ per decade（p 〈 0.01） across SWC.The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province.Growing season average sunshine hours decreased by 0.2 h day^（-1） per decade（p 〈 0.01） and total precipitation showed an insignificant decreasing trend across SWC.Photosynthetic potential productivity decreased by 298 kg ha^（-1）per decade（p 〈 0.05）.Both light-temperature and climatic potential productivity decreased（p 〈 0.05） in the northeast of SWC,whereas they increased（p 〈 0.05） in the southwest of SWC.The gap between lighttemperature and climatic potential productivity varied from 12 to 2729 kg ha^（-1）,with the high value areas centered in northern and southwestern SWC.Climatic productivity of these areas reached only 10%-24%of the light-temperature potential productivity,suggesting that there is great potential to increase the maize potential yield by improving water management in these areas.In particular,the gap has become larger in the most recent 10 years.Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC.The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.

Role of renewable,non-renewable energy consumption and carbon emission in energy efficiency and productivity change:Evidence from G20 economies

The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries.Energy Efficiency(EE)is crucial in achieving sustainable economic growth while reducing ecological impacts.This research utilizes the Slack-Based Measure Data Envelopment Analysis(SBM-DEA)and the Malmquist-Luenberger Index(MLI)method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries.The study uses four different input-output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes.The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578,but energy productivity declines from 1.0064 to 0.9988.Incorporating bad output(carbon emissions)in the estimation process enhances renewable EE and productivity change,resulting in an average EE of 0.6678 and MLI of 1.0044.Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2,while technical efficiency determines energy productivity change in scenarios 3 and 4.The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.

Spatial impacts of climate factors on regional agricultural and forestry biomass resources in north-eastern province of China

The dynamics of agricultural and forestry biomass are highly sensitive to climate change, particularly in high latitude regions. Heilongjiang Province was selected as research area in North-east China. We explored the trend of regional climate warming and distribution feature of biomass resources, and then analyzed on the spatial relationship between climate factors and biomass resources. Net primary productivity （NPP） is one of the key indicators of vegetation productivity, and was simulated as base data to calculate the distribution of agricultural and forestry biomass. The results show that temperatures rose by up to 0.37℃/10a from 1961 to 2013. Spatially, the variation of agricultural biomass per unit area changed from -1.93 to 5.85 t.km^-2.a^-1 during 2000,2013. More than 85% of farmland areas showed a positive relationship be.tween agricultural biomass and precipitation. The results suggest that precipitation exerts an overwhelming climate influence on agricultural biomass. The mean density of forestry biomass varied from 10 to 30 t·km^-2. Temperature had a significant negative effect on forestry biomass in Lesser Khingan and northern Changbai Mountain, because increased temperature leads to decreased Rubisco activity and increased respiration in these areas. Precipitation had a significant positive relationship with forestry biomass in south-western Changbai Mountain, because this area had a wanner climate and stress from insufficient precipitation may induce xylem cavitation. Understanding the effects of climate factors on regional biomass resources is of great significance in improving environmental management and promoting sustainable development of further biomass resource use.