Modeling the fiscal impacts caused by climate change

Aim of the paper: The purpose is to gather the practices and to model the impacts of climate change on fiscal spending and revenues, responsibilities and opportunities, balance and debt related to climate change (CC). Methodology of the paper: The methodology will distinguish fiscal cost of mitigation and adaptation, besides direct and indirect costs. It will also introduce cost benefit analyses to evaluate the propensity of policy makers for action or passivity. Several scenarios will be drafted to see the different outcomes. The scenarios shall contain the possible losses in the natural and artificial environment and resources. Impacts on public budget are based on damage of income opportunities and capital/wealth/natural assets. There will be a list of actions when the fiscal correction of market failures will be necessary. Findings: There will be a summary and synthesis of estimation models on CC impacts on public finances, and morals of existing/existed budgeting practices on mitigation. The model will be based on damages (and maybe benefits) from CC, adjusted with probabilities of scenarios and policy making propensity for action. Findings will cover the way of funding of fiscal costs. Practical use, value added: From the synthesis of model, the fiscal cost of mitigation and adaptation can be estimated for any developed, emerging and developing countries. The paper will try to reply, also, for the challenge how to harmonize fiscal and developmental sustainability.

Does Lost Time Cost You Money and Create High Risk?

The aim of the case study is to express the delayed repair time impact on the revenues and profit in numbers with the example of the outage of power plant units. Main steps of risk assessment: • creating project plan suitable for risk assessment • identification of the risk factors for each project activities • scenario-analysis based evaluation of risk factors • selection of the critical risk factors based on the results of quantitative risk analysis • formulating risk response actions for the critical risks • running Monte-Carlo simulation [1] using the results of scenario-analysis • building up a macro which creates the connection among the results of the risk assessment, the production plan and the business plan.

Trust - Networking - Innovation

From innovation point of view the agri-food industry is seen as matured branch of the economy, where revolutionary new products and processes are very rare. Especially the SMEs are in squeezing situation: they have to fit very sharp prerequisites and demands on one side and very much constrained resources to give them power in order to formulate appropriate answers on the other side. They are looking for partners beyond the boundaries of their organization, mainly with other firms, universities, research organisations and government agencies. Adopting an effective innovation process to successfully introduce and develop new products to the market has become one of the most important strategies for food companies. The innovation dimension of networking activity contributes to growing network complexity, which in turn also affects the nature of traditional governance structure. Trust and other relational factors are playing an increasing role in these structures. Our research interest is whether the trust as coordination form of governance structure plays significant role in the Hungarian agri-food industry. Empirical data was drawn from a survey carried out in Central Hungary and aiming at the research of cooperation and knowledge management within the SMEs of the food economy. Structural Equation Modelling (SEM) is applied in order to determine the relationship among the three (Trust, Networking, Innovation) latent factors. We have found that trust plays significant positive role in increasing networking activity and innovation, but the extent of it is less than expected.

Modeling the future distribution of Mediterranean Pinus species

The potential future distribution of four Mediterranean pines was aimed to be modeled supported by EUFORGEN digital area database (distribution maps), ESRI ArcGIS 10 software’s Spatial Analyst module (modeling environment), PAST (calibration of the model with statistical method), and REMO regional climate model (climatic data). The studied species were Pinus brutia, Pinus halepensis, Pinus pinaster, and Pinus pinea. The climate data were available in a 25 km resolution grid for the reference period (1961-90) and two future periods (2011-40, 2041-70). The climate model was based on the IPCC SRES A1B scenario. The model results show explicit shift of the distributions to the north in case of three of the four studied species. The future (2041-70) climate of Western Hungary seems to be suitable for Pinus pinaster.