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As a low-carbon transport mode, the railway has a significant influence on transport CO₂ emissions. This paper abstracts and quantifies the influence from a new perspective to explore the influence of the railway industry on transport CO₂ emissions. Considering various influencing factors systematically, we establish the transport CO₂ emissions correlation (TCEC) network based on the theory of complex networks. The superimposed influence diffusion (SID) model proposed in this paper can predict the future trend of the transport CO₂ emissions through network evolution, which is different from traditional methods. The railway-related scenarios are designed to analyze the influence spread and the possibility of Beijing peaking the transport CO₂ emissions before 2035. The research results show that the transport CO₂ emissions reach a peak and then slowly decrease under four scenarios, while under other scenarios, the transport CO₂ emissions keep growing unable to achieve a peak. The proposed methods can be extended to other areas, and the research findings have certain reference for making policies.

The analysis of the effects of fertilizer pricing on fertilizer-intensive agriculture and farms behavior ought to be an important topic of research for agricultural and environmental economists in Bangladesh. Several possibilities for fertilizer policy have been debated, in particular for the pricing of fertilizer. Following this observation, this study contributes to that discussion by simulating the impact that various policies based upon the price of fertilizer could have on agricultural production. Specifically, the study analyzes the economic, social and environmental implications of alternative fertilizer policies using a multicriteria model of farmers' behavior under different scenarios. The future agricultural and fertilizer scenarios are described in terms of the combination of policy instruments, policy style and configuration of actors. For the purpose of scenario analysis, narratives and quantitative indicator values have been compiled for each scenario. The quantitative estimates are used as input values in the modeling of fertilizing systems under policy changes. The results show that the increase of fertilizer price causes almost similar impacts as observed in the status quo scenario. The results also stress that fertilizer pricing, as a single instrument for controlling fertilizer use is not a satisfactory tool for significantly reducing fertilizer consumption in agriculture.

Software system analysis for identifying software functionality in source code remains a major problem in the reverse engineering literature. The early approaches for extracting software functionality mainly relied on static properties of software system. However, the static approaches by nature suffer from the lack of semantic and hence are not appropriate for this task. This paper presents a novel technique for dynamic analysis of software systems to identify the implementation of certain software functionality known as software features. In the proposed approach, a specific feature is shared by a number of task scenarios that are applied on the software system to generate execution traces. The application of a sequential pattern mining technique on the generated execution traces allows us to extract execution patterns that reveal the specific feature functionality. In a further step, the extracted execution patterns are distributed over a concept lattice to separate feature-specific group of functions from commonly used group of functions. The use of lattice also allows for identifying a family of closely related features in the source code. Moreover, in this work we provide a set of metrics for evaluating the structural merits of the software system such as component cohesion and functional scattering. We have implemented a prototype toolkit and experimented with two case studies Xfig drawing tool and Pine email client with very promising results.

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change — and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e. fairness/meritocracy). Moreover, in many fields, humans are overconfident and pervasively confuse luck for skill (I win, it is skill; I lose, it is bad luck). In some fields, there is too much risk-taking; in others, not enough. Where success derives in large part from luck — and especially where bailouts skew the incentives (heads, I win; tails, you lose) — it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short-term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk-adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration–exploitation trade-off. Several human endeavors — including finance, politics, and science — are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.

A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

Forecasting the future is an important aspect of strategic planning. In recent years, an increasing number of corporate planners and forecasters have been turning to forecasting techniques to assist them in management decision-making. This paper presents a fuzzy management decision support system for scenario analysis. The system, developed in dBFAST, comprises two modules: data input and scenario generation. It is aimed at emulating the expert’s reasoning process in forecasting. It adopts a hybrid technique—a combination of fuzzy Delphi analysis and fuzzy reasoning—for problem solving. Three widely accepted forecasting techniques—the Delphi method, trend impact analysis and cross-impact analysis—are briefly reviewed. The hybrid technique developed and the details of the fuzzy management decision support system for scenario analysis are described. Using a case study on the penetration of computer-integrated manufacturing (CIM) in the electronics industry in Singapore as an example, the performance of the system is discussed.

This paper deals with the practice and requirements of strategic foresight in biotechnology firms. Processes and the degree of method application of strategic foresight are less investigated in small- and medium-sized enterprises. Based on case study research of 30 biotechnology companies in Germany, six different approaches of strategic foresight are identified. The study shows how strategic foresight is organised, which methods for strategic foresight are implemented, who is responsible for strategic foresight, what the main characteristics of the different approaches are, and how the strengths and weaknesses of strategic foresight practice in biotechnology firms can be characterized. Furthermore, firms' requirements for suitable foresight processes and methods are identified within the scope of case study research.

Literature on business models deals extensively with how to design new business models, but hardly with how to make the transition from an existing to a newly designed business model. The transition to a new business model raises several practical and strategic issues, such as how to replace an existing value proposition with a new one, when to acquire new resources and capabilities, and when to start new partnerships. In this paper, we coin the term business model roadmapping as an approach to define the transition path from a current to a desired business model. We develop our approach based on core concepts from business model literature as well as technology roadmapping. The approach is illustrated using a simplified case study. We find that visualizing business model road maps elicits how operational actions and business model impacts are interrelated. The merits of business model roadmapping not only lie in defining a road map of actions and business model changes, but also in identifying and discussing trade-offs between strategic business model issues and operational activities. Especially if an organization still has to choose between different alternative business models, business model roadmapping may help to identify overlapping paths, path dependencies and points of no return.

Ecosystem management requires understanding society's goals for an ecosystem and managing for some optimal solution. Unlike terrestrial ecosystem managers, coastal and marine ecosystem management seldom integrates across sectors or scientific disciplines to achieve desired social benefits. An Integrated Ecosystem Assessment (IEA) considers the ecosystem (including humans) as a unit and can assist in setting goals, determining an ecosystem's ability to support ecological processes and society's desires, and predicting the outcome of alternatives. The use of Coupled Ecological-Societal Systems Models utilised within the Integrated Assessment and Ecosystem Management Protocol (IAEMP) allows managers to extend a graphical picture of risk hypotheses to forecast scenarios that can be analysed relative to management goals. Scenarios predicted to meet management goals are evaluated against management constraints to select the "optimal" option for a management action in an adaptive management process. The IAEMP thus helps characterise potential causes of environmental problems, select appropriate response options, and implement and evaluate the selected option, thereby either addressing the concern or improving the ecosystem model for future decisions.

In order to support Strategic Environmental Assessment of spatial plans, different scenarios were developed for the future configuration of wetlands along the coast of La Araucanía Region for 2020. To assess each scenario, landscape metrics related to landscape dynamics and structure were used. The results indicate that in general terms the wetland cover diminished and fragmented under different scenarios, including one which was designed for the sustainability of natural areas. It is concluded that the techniques used were relatively easy to implement by means of GIS technologies, which facilitate spatially explicit modelling of future scenarios. Furthermore, landscape metrics were a key element for assessing the effects of each model. There are currently only few experiences on the use of spatially explicit scenarios in SEA and our research suggests that this may be a useful and valid tool for supporting spatial planning decisions.

Multi-criteria decision analysis (MCDA) of regional level long-term forest management scenarios was conducted by applying spatially explicit information to examine the trade-offs between ecological, economic and social impacts. Interval scale judgements were applied to mapped and numerical information jointly, while assessing the performance of alternative scenarios. The experts relied mostly on the numerical information, with which they might have been more confident and familiar. The weight elicitation was based on SMART using Swings (SMARTS) and SMART Exploiting Ranks (SMARTER). SMARTS resulted in two scenarios being quite equal either due to experts truly considering them equally important or being unconfident to express their weights applying SMARTS. SMARTER was considered more understandable, but lead to total utilities having wider range. However, impact information had a greater effect on the overall utility than the weighting. Future insights include use of dynamic approach, considering the issue more from ecosystem services point of view and tighter integration of participatory MCDA and geographic information systems (GIS).

This paper analyzes the structure of energy consumption in Iran. The demand side is divided into five major categories. Each category is further classified into subcategories and modelling is accomplished for the time horizon of 2012 to 2041. In order to determine final results, six technical and economic scenarios have been considered, entailing economic development scenarios and governmental energy management programs. Results show that final energy demand in Iran in all scenarios has an ever growing trend. It is expected that the total emission on demand side reaches to the range of 572 to 856 million tons in 2041. Results also indicate that there is a high potential for carbon reduction in energy demand side up to 5.68% annually.

Despite their promises of wealth creation, productivity increase, and improved living circumstances in Africa, industrialization, and foreign direct investment (FDI) inflows have the potential to endanger the continent’s climate, particularly given the nature of energy intensity and associated emissions connected with their expansion. Thus, this study empirically examined the extent to which industrialization and FDI inflows contribute to the predictability of climate change in Africa, focusing on the top 10 greenhouse gas (GHG) emitting countries on the continent with data spanning from 1990 to 2023. Employing a bias-adjusted ordinary least square estimation technique, we considered both in-sample and out-of-sample forecasts that include several scenario analyses. We reveal both industrialization and FDI inflows as significant predictors of climate change in Africa but with varying degrees of environmental threats. This provides the foundation for, among other things, the suggestion that African continental and sub-regional bodies consider the need for differences in climate commitment strategies across the region based on the varying nature and magnitude of manufacturing intensity and FDI inflows, as well as associated GHG emissions and the nature of climate change vulnerability.

Investing in a Time of Climate Change — The Sequel (the Sequel) documents Mercer’s latest climate scenario model for assessing the effects of both climate-related physical damages (physical risks) and the transition to a low-carbon economy (transition risks) on investment return expectations. The Sequel models three climate change scenarios, a 2°C, 3°C, and 4°C average warming increase on preindustrial levels, over three timeframes — 2030, 2050, and 2100…

The Carbon Tracker Initiative is a London-based non-for-profit independent financial think tank established in 2011 and researches the impact of climate change and energy transition on the financial markets. Its team of financial market, energy and legal experts are focused on raising the awareness of the impact that an energy transition to a low-carbon future will have for fossil fuel companies, investors and the capital markets…

2° Investing Initiative (2°ii) is a global think tank whose mission is to align the financial markets with the goals of the Paris Agreement.

We estimate the carbon emissions of Hubei Province from 1997 to 2019 based on the Hubei energy balance sheets and national and Hubei statistical yearbook data by the IPCC Emission Factor Method. We construct the carbon peak prediction model for Hubei province based on the STIRPAT extended model to predict the peak carbon emissions and peak time in Hubei province from 2020 to 2050 under different scenarios. Results show that the overall carbon emissions in Hubei Province have been increasing, and the average increase was about 3.8%, but the carbon intensity is decreasing year by year, and the average decline was about 7.9%. The peak time of the low-speed development scenario is 2022, and its peak value is 343.6865 million tons. The peak time of the medium-speed development scenario is 2026, and its peak value is 361.9586 million tons; The peak time of the high-speed development scenario is 2030, and its peak value is 374.5220 million tons. The population size and economic development affect carbon emissions in Hubei Province. The construction of a scientific and technological support system and the adjustment of the energy structure and industrial structure can promote Hubei Province to achieve the carbon peak as soon as possible.

In many psychological questionnaires (i.e., personnel selection surveys and diagnostic tests) the collected samples often include fraudulent records. This confronts the researcher with the crucial problem of biases yielded by the usage of standard statistical models. In this paper we generalize a recent combinatorial perturbation procedure, called SGR (Sample Generation by Replacements; [Lombardi et at., 2004]), to the analysis of structured malingering scenarios for dichotomous data. Combinatorial aspects of the approach are discussed and an application to a simple data set on the drug addiction domain is presented. Finally, the close relationships with Monte Carlo simulation studies are explored.

Being a new form of civilization more advanced than the industrial civilization, the concept of ecological civilization has attracted increasing attention in China. However, the challenge is using the advanced theory to guide decision making. Based on statistical data and characteristics of the Yanqing district in Beijing, the industrial structure and gross domestic product has been studied under three scenarios: business as usual model, industry development strategy, and ecological civilization model. Results show that the economy and the three industry structure have the best performances based on the ecological civilization model. Taking advantage of Yanqing’s own ecological asset, the new energy industry, ecological tourism industry, exhibition services industry, health consulting industry et al. will be the pillar industry in the future. With this, the ecological environment will receive the most extensive protection, while the economy rapidly develops at the same time.