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• Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. • We tested a new profit maximization model, where photosynthetic uptake of CO₂ is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil–plant continuum k max (a key model parameter which is not commonly measured) could be predicted from long-term site climate. • Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that k max could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. • Although the generalization of this approach is contingent upon determining k max, it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

Directional distance function (DDF) has been a commonly used technique for estimating efficiency and productivity over the past two decades, and the directional vector is usually predetermined in the applications of DDF. The most critical issue of using DDF remains that how to appropriately project the inefficient decision-making unit onto the production frontier along with a justified direction. This paper provides a comprehensive literature review on the techniques for selecting directional vector of the directional distance function. It begins with a brief introduction of the existing methods around the inclusion of the exogenous direction techniques and the endogenous direction techniques. The former commonly includes arbitrary direction and conditional direction techniques, while the latter involves the techniques for seeking theoretically optimized directions (i.e., direction towards the closest benchmark or indicating the largest efficiency improvement potential) and market-oriented directions (i.e., directions towards cost minimization, profit maximization, or marginal profit maximization benchmarks). The main advantages and disadvantages of these techniques are summarized, and the limitations inherent in the exogenous direction-selecting techniques are discussed. It also analytically argues the mechanism of each endogenous direction technique. The literature review is end up with a numerical example of efficiency estimation for power plants, in which most of the reviewed directions for DDF are demonstrated and their evaluation performance are compared.

Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We mainly consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O (1) time, and a ( 1 - 1 / e ) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could achieve a ( 1 - e - ( 1 - ϵ ) ) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies.

In Europe, at least 3 GW installed capacity of wind turbine generators (WTG) will fall out of subsidy schemes every year from 2021 onwards. An estimated 50% of this capacity cannot be replaced with new WTG due to commercial and legal restrictions. The remaining options are either to sell the electricity without subsidies on the wholesale electricity market—a novelty for most WTG, as most are receiving a feed-in tariff—or their dismantlement. Since the electricity market fixes the price at the intersection of demand and short run marginal production costs, WTG might struggle to generate enough revenues to cover their costs. This paper proposes an innovative commercialization strategy for WTG after the end of the feed-in tariff, namely a profit-maximized approach that focuses on synergies between revenues and costs when increasing the curtailments of the WTG. The two key elements of this approach are a more flexible and variable cost structure and a central overall optimization process. The paper proves the potential of this new strategy and highlights the necessity of further research for WTG at the end of their lifetime from a technical and commercial perspective, due to the impact on the initial investment decision and best allocation of subsides.

Electricity systems are quitting monopoly for an operation framed by competitive markets. It therefore questions how the operation of nuclear plants capable of load-following is expected to change at the transition stage in a challenging economic environment for nuclear producers given the high penetration of renewables and the increase of nuclear production costs for renovation and safety reasons. To answer this question, we are focusing on a month-by-month management horizon of the nuclear fuel given the technico-economical features of the operation of flexible nuclear plants. The resolution of the month-by-month profit maximization problem contributes to identify potential difficulties and limits of production optimization and provides us with an equilibrium which proves the existence of an optimal production path. This benchmark could serve as a reference providing valuable insight on the resolution of more complex optimisation problems.

In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if β percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

This article empirically investigates how Chinese executives and managers perceive and interpret corporate social responsibility (CSR), to what extent firms' productive characteristics influence managers' attitudes towards their CSR rating, and whether their values in favour of CSR are positively correlated to firms' economic performance. Although a large proportion of respondents express a favourable view of CSR and a willingness to participate in socially responsible activities, we find that the true nature of their assertion is linked to entrepreneurs' instincts of gaining economic benefits. It is the poorly performing firms, or rather, firms with vulnerable indicators -smaller in size, State-owned, producing traditional goods and located in poorer regions that are more likely to have managers who opt for a higher CSR rating. Managers' personal characteristics per se are not significant in determining their CSR choice. Moreover, controlling for other observed variables, we find that managers' CSR orientation is positively correlated with their firms' performance. The better-off a firm is, the more likely its manager is to get involve in CSR activities. Firms with better economic performance before their restructuring would sustain higher postrestructuring performance.

Many researchers believe the tremendous industrial development over the past two centuries is unsustainable because it has led to unintended ecological deterioration. Despite the ever-growing attention sustainable supply-chain management (SSCM) has received, most SSCM research and models look at the consequences, rather than the antecedents or motives of such responsible practices. The few studies that explore corporate motives have remained largely qualitative, and large-scale empirical analyses are scarce. Drawing on multiple theories and combining supply-chain and business ethics literature, we purport that instrumental, relational, and moral motives are behind a firm's engagement in SSCM practices. Specifically, we examine the links between corporate motives, SSCM practices, and firm performance. Using a sample of 259 supply-chain firms in Germany, we empirically test five hypothesized relationships. Our results reveal that relational and moral motives are key drivers, and that firms exhibiting high levels of moral obligations tend to outperform those primarily driven by amoral considerations. Findings of this study contribute to multiple literatures espousing sustainability management and can help policy makers, stakeholder groups, and scholars develop more robust strategies for encouraging firms to practice SSCM.

The text currently reads “Use Standard GRG Nonlinear Engine” in sections “Stage 1 model” and “Stage 2 model,” but it should read “Use Standard Evolutionary Engine” in both places.

In this article we analyse the short run profit maximization problem in a convex analysis framework. The goal is to apply the results of convex analysis due to unique structure of microeconomic phenomena on the known short run profit maximization problem where the results from convex analysis are deductively applied. In the primal optimization model the technology in the short run is represented by the short run production function and the normalized profit function, which expresses profit in the output units, is derived. In this approach the choice variable is the labour quantity. Alternatively, technology is represented by the real variable cost function, where costs are expressed in the labour units, and the normalized profit function is derived, this time expressing profit in the labour units. The choice variable in this approach is the quantity of production. The emphasis in these two perspectives of the primal approach is given to the first order necessary conditions of both models which are the consequence of enveloping the closed convex set describing technology with its tangents. The dual model includes starting from the normalized profit function and recovering the production function, and alternatively the real variable cost function. In the first perspective of the dual approach the choice variable is the real wage, and in the second it is the real product price expressed in the labour units. It is shown that the change of variables into parameters and parameters into variables leads to both optimization models which give the same system of labour demand and product supply functions and their inverses. By deductively applying the results of convex analysis the comparative statics results are derived describing the firm's behaviour in the short run.