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Graphical abstract Highlights • Structures of industrial sectors have significant impacts on energy intensity. • The energy intensity may be affected by uncertainties in industrial activities. • The randomness of energy intensity may lead to unexpected violation events. • Copula-based violation risk analysis can support decision-making in energy-intensity mitigation. Abstract An integrated approach was developed through incorporating a copula-based violation risk analysis into a programming model for optimizing industrial structures of urban clusters in uncertain conditions. Also, this approach can be used to support decision making about promoting advanced manufacturing sectors (AMSs) and mitigating industrial energy intensity. The advantages and the improvements of this approach lie in (a) reflecting the relationships between energy consumption and economic benefits in industrial sectors, and (b) incorporating the violation risk of the targets in energy-intensity mitigation into an optimizing model. A case study was conducted to illustrate the application of this approach in the Pearl River Delta of China, a highly urbanized area that includes the cities of Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan, and Jiangmen. The results indicated that under the desired industrial structures, violation risk of energy-intensity mitigation in the urban cluster of the Pearl River Delta would be indistinctive in the single-city perspective. Also, except Dongguan, Jiangmen and Zhaoqing, the cities of the urban cluster would achieve the goal for developing AMSs, based on the Industrial Plans for the 13th Five Year. [ABSTRACT FROM AUTHOR]

To improve the capabilities of conventional methodologies in supporting greenhouse gas (GHG) emission mitigation from food production under uncertain conditions, an integrated approach was developed through incorporating copula-based violation risk analysis into a general life cycle analysis (LCA) framework. This approach strengthened the applicability of LCA in terms of uncertainty and risk reflections for food systems in the background of urbanization under multiple GHG emission targets. In detail, such an approach can (a) reflect uncertainties of food production and consumption processes, (b) tackle joint probability of two correlated variables (i.e., GHG emissions and economic benefits) through the employment of Archimedean copula (i.e., Gumbel, Frank, and Clayton copula) and Gaussian copula, and (c) assess violation risk of GHG emissions in food production considering the trend of future urbanization. A case study was proposed to illustrate application of the approach in Dalian City, China. Considering uncertainties of dietary patterns in 2020, two scenarios (scenarios of baseline and increase) were proposed according to dietary structures of urban and rural residents. In detail, the scenario of baseline represented the recent dietary pattern of Dalian in 2015. Scenario of increase indicated 50% rise in beef, mutton, and milk compared with the dietary pattern under the scenario of baseline. The results showed that based on the current agenda upon GHG emission intensity in China, violation risks under the scenario of baseline would be more prominent than the violation risks under the scenario of increase. [ABSTRACT FROM AUTHOR]