The use of landslide hazard maps to determine mitigation priorities in a dam reservoir and its protection area

Despite longstanding research on the landslide hazard assessment and mapping, the use this information in regional planning applications remains largely unclear. There is still a need for novel methods to interpret the results of hazard analyses. In this respect, the main research question of this study is how the landslide hazard maps can be evaluated in planning practice. To evaluate this issue, a comprehensive case study was carried out and a new methodology, defining the order of priority for the locations where mitigation measures are applied was developed. The investigations were carried out in the Melen Dam reservoir, which provides potable water to the city of Istanbul, and the dam protection area (Duzce, Turkey) in four main stages: (i) Evaluation of the general characteristics of the study area, (ii) preparation of landslide inventory, (iii) development of landslide hazard model, and (iv) implementation of the results of hazard analyses in planning. As a consequence, a decision support system that can evaluate analytically complicated outputs “P_Combined”, “P_{AL min.}”, and “L_Total” and provide clear decisions “Urgent”, “Primary”, and “Secondary” was suggested. Considering the limited resources available for the mitigation measures, the determination of mitigation priorities for the micro-catchments constitutes the key land-use policy for the protection of the dam reservoir.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

Justifying ridge regression from a geometrical perspective is one of the main contributions of this paper. To the best of our knowledge, this question has not been treated previously. This paper shows that ridge regression is a particular case of raising procedures that provide greater flexibility by transforming the matrix X associated with the model. Thus, raising procedures, based on a geometrical idea of the vectorial space associated with the columns of matrix X , lead naturally to ridge regression and justify the presence of the well-known constant k on the main diagonal of matrix X ^′ X . This paper also analyses and compares different alternatives to raising with respect to collinearity mitigation. The results are illustrated with an empirical application.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

New ventures in Cleantech: Opportunities,capabilities and innovation outcomes

Facing the challenge of climate change, innovations that imply environmental benefits create business opportunities for entrepreneurs. This paper analyzes innovation capabilities of startups in Cleantech and how the innovation outcomes of those startups develop over time. Based on the Mannheim Foundation Panel and applying propensity score matching, a cohort of 567 Cleantech startups is analyzed and compared with a control cohort of non‐Cleantech startups. We find that startups in Cleantech have, on average, higher technological capabilities compared with all other startups. Our econometric evidence shows that Cleantech startups are more likely to combine existing technology in a novel way. Finally, we find that Cleantech startups develop more market novelties in subsequent years when compared with their control group peers.     

Disentangling the drivers of renewable energy investments: The role of behavioral factors

The scope of this study is to investigate the main determinants of renewable energy investments in Greece. For this reason, we build an appropriate framework to infer the decision‐making process in the renewable energy sources (RES). The main drivers are then categorized under two groups as economic, and behavioral ones. In the next stage, we rely on binomial (logit and probit) and quantile regression analysis to estimate the impact of these factors on investment decisions towards RES. Our findings indicate that investors who gain better access to knowledge and exhibit a more positive attitude towards the contribution of artificial intelligence (AI) on the RES industry have a higher share of renewables in their portfolio. We argue that these investors are willing to invest a higher amount in the RES industry today. At the same time, quantile regression models illustrate that this relationship is nonmonotonic (i.e., inverted U shaped curve), arguing that positive attitudes towards the contribution of behavioral factors on RES are not adequate to encourage investments after crossing a certain point (threshold). Based on the empirical findings, we argue that the message to policymakers and government officials is to promote a faster penetration of low carbon technologies to achieve sustainable economic and social growth.     

地方政府债务对工业资本配置效率的非线性影响效应——基于动态面板平滑转换回归PSTR模型的检验

经济新常态下,提高工业资本等要素配置效率是实现经济向高质量增长转变的必然要求。本文在政府主导、投资驱动的工业发展模式下,结合地方债务压力和金融发展差异门限变量,在非线性框架下运用动态面板平滑转换回归模型实证检验地方债务规模与工业资本配置效率的渐进演变关系。研究发现,适度的地方债务规模有助于提高资本配置效率,但随着债务压力增加,举债对资本配置的正效应逐步减弱并产生负效应。究其原因,适度举债能补齐工业基础设施短板,压低土地成本,对工业发展产生杠杆效应,从而提高资本配置效率;但过度举债推升财政风险,占用信贷资源并强化企业融资约束,造成资本配置低效率。此外,研究发现,地方信贷规模提升,能缓解举债的融资约束,促进工业行业间的资本流动;而工业金融深化程度提高,能增强市场竞争机制在要素配置中的作用,缓解举债造成投资错配,从而增强资本配置效率。以上分析结果表明,地方政府举债应更理性、适度、规范。