Many effects of interest to sociologists are nonlinear. Additionally, many effects of interest are interaction effects—that is, the effect of one independent variable is contingent on the level of ...another independent variable. The proper way to estimate, interpret, and present these two types of effects individually are well known. However, many analyses that combine these two—that is, tests of interaction when the effects of interest are nonlinear—are not properly interpreted or tested. The consequences of approaching nonlinear interaction effects the way one would approach a linear interaction effect are severe and can often result in incorrect conclusions. I cover both nonlinear effects in the context of linear regression, and—most thoroughly—nonlinear effects in models for categorical outcomes (focusing on binary logit/probit). My goal in this article is to synthesize an evolving methodological literature and to provide straightforward advice and techniques to estimate, interpret, and present nonlinear interaction effects.
The previous literature presents conflicting outcomes on the relationship between financial development and CO2 emissions. This study fixes this puzzle by testing both the direct and indirect effects ...of financial development on environmental pollution using Environmental Kuznets Curve (EKC) framework. Our empirical investigation relies upon difference and system generalized method of moments for a large sample of 88 developing countries during 2000–2014 period. The estimated outcomes, based on five different indicators of financial development, support the pollution inhibiting role of financial development for the selected countries. We also validate the existence of EKC hypothesis for the panel of economies. More importantly, the results of the indirect channels show that financial development also reduces the adverse effects of income, trade openness and FDI on the pollution emissions. Further, the validity of pollution heaven hypothesis (PHH), tested through trade openness and FDI variables, is also contingent upon the existence of weak financial structure. When financial development traverses certain limits, PHH ceases to exist for both these variables. Lastly, population size augments pollution emissions while human capital reduces the later. Based on these results, we propose some very important policy implications for the sample economies.
•Effects of financial development on environmental pollution for 88 developing countries examined.•The results support the pollution inhibiting role of financial development.•Environmental Kuznets Curve hypothesis for the panel of economies is validated.•Financial development reduces the adverse effects of income, trade openness and FDI on the pollution emissions.•Population size augments pollution emissions while human capital reduces the later.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by ...Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. (2013) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.
Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact ...to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.
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•Sequence variants can have complex effects on lipid levels through interactions•Variants at FUT2 and ABO interact on blood lipid levels and coronary artery disease•Alcohol consumption modifies the effect of a variant in ADH1B on lipids and CAD•Blood lipid composition in APOE-ε2 homozygotes differs substantially from others
Epistatic interactions between variants and gene-environment interactions can have complex phenotypic effects. By using blood lipid data from 743,736 individuals from three populations, it was shown here how these intricate interactions can have large effects on lipid levels and consequently coronary artery disease risk.
•Quantify the interaction between urban ecological land and environmental factors.•Urban ecological land show spatiotemporal coupling with surface urban heat island.•The average explanatory power of ...patch dominance and richness was 19.95% and 16.03%.•Interaction of urban ecological land with topography rapidly increased in 2015–2020.•Interaction between urban ecological land and anthropogenic factors was dominant.
The surface urban heat island effect (SUHI) that occurs during rapid urbanization increases the health risks associated with high temperatures. Urban ecological land (UEL) has been shown to play an important role in improving urban heat stress, however, the impact of UEL interactions with the natural-anthropogenic environment on SUHI at the urban agglomeration-scale is less explored. In this study, the Google Earth Engine and GeoDetector were applied to characterize the spatiotemporal patterns of UEL and SUHI in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2020 by extracting major built-up urban areas and quantifying the impacts of UEL and its interactions with the natural-anthropogenic factors on SUHI. The results show that the evolution of the UEL landscape structure exhibits clear spatiotemporal coupling with SUHI. Specifically, the UEL underwent a dispersion and degradation process in 2000–2015 and a convergence and restoration process in 2015–2020, the SUHI correspondingly transitioned from intensification and continuity to mitigation and contraction. The UEL landscape structure showed a notable impact on the SUHI reduction, and the dominance and richness of the patches explained an average of 19.95% and 16.03% of the SUHI, respectively. Moreover, the interaction between UEL and land urbanization rate and anthropogenic heat release had a dominant effect on SUHI, but this effect significantly declined from 2015 to 2020. With the implementation of ecological restoration projects, the interaction of UEL with topography rapidly increased and the SUHI gradually dominated by the joint interaction of UEL and natural-anthropogenic factors. A synthesis of the varying effects of several factors showed that the dynamic relationship between the development stages of the urban agglomeration’s regional system and SUHI may conform to the Environmental Kuznets Curve. SUHI reduction strategies should therefore comprehensively optimize the rational allocation of UEL landscape structures and natural-human elements to promote the well-being of residents.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The effect of ambient temperature on EV energy consumption is modelled.•The interactive effects of ambient temperature and auxiliary loads is explored.•Energy consumption of the heater during warm ...conditions is conventionally exaggerated.•Energy consumption of air conditioner during cold days is typically underestimated.•Eradicating unreasonable EV auxiliary loads yields an average savings of 9.66% per km.
The ability to accurately predict the energy consumption of electric vehicles (EVs) is important for alleviating the range anxiety of drivers and is a critical foundation for the spatial planning, operation and management of charging infrastructures. Based on the GPS observations of 68EVs in Aichi Prefecture, Japan, an energy consumption model is proposed and calibrated through ordinary least squares regression and multilevel mixed effects linear regression. Specifically, this study focuses on how the ambient temperature affects electricity consumption. Moreover, the interactive effects of ambient temperature and vehicle auxiliary loads are explored. According to the results, the ambient temperature affects the energy efficiency significantly by directly influencing the output energy losses and the interactive effects associated with vehicle auxiliary loads. Ignoring the interactive effects between ambient temperature and vehicle auxiliary loads will exaggerate the energy consumption of the heater during warm conditions and underestimate the energy consumption of the air conditioner during cold conditions. The most economic energy efficiency was achieved in the range of 21.8–25.2°C. The potential energy savings during proper usage of vehicle auxiliary loads is discussed later based on estimated parameters. Asa result, a mean of 9.66% electricity will be saved per kilometre by eradicating unreasonable EV auxiliary loads.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Microplastics (MPs) ‒ PET, PVC, and PS ‒ disrupt microalgal metabolism.•MPs affect microalgal carbon allocation thereby impacting bioeconomy.•MPs pollution is linked so far typically to UN SDG 14 ...(Life Below Water).•Microalgae–MPs interactions significantly impact additional five SDGs.•Understanding microalgae–MPs interactions aids sustainable future strategies.
Microplastics (MPs) are one of the emerging pollutants, causing potential harm to aquatic ecosystems and serious concern in achieving UN Sustainable Development Goals (SDGs). Realizing the occurrence of varying concentrations of MPs in the environment, this investigation presents multi-dimensional insights into the ecological and bioeconomic implications at environmentally relevant concentrations. We pursued a multi-step approach to gain a comprehensive understanding on the effects of microalgae‒MPs interactions and their expansive implications toward SDGs. Baseline data generated using a model microalga, Raphidocelis subcapitata, and three MPs (polyethylene terephthalate, PET; polyvinyl chloride, PVC; and polystyrene, PS) indicated 10‒15% reduction in microalgal growth rate relative to the control, pointing to a heightened energy demand. The biochemical impacts displayed concentration-dependent variability. Using the baseline data, we developed a linear regression model to dissect the interaction effects around the primary dimensions of Ecology and Bioeconomy. Notably, a correlation matrix for carbon allocation pinpointed PET as having a more pronounced impact compared to PVC and PS, with the model accounting for 33.72% of the observed variance. Extending our insights from the model, we adopted an evidence-based methodology to outline the broader implications across the Ecology and Bioeconomy domains, and subsequently identified their associations with specific SDGs. Further probing into microalgae‒MPs interaction effects at environmentally relevant concentration, our model revealed that the selected MPs perturbed the ecological variables. Interestingly, when carbon allocation was assessed to study bioeconomic implications, there were contrasting effects on starch synthesis (beneficially) and lipid synthesis (detrimentally). The present combined analysis revealed that MPs, beyond their traditional association with SDG 14 (Life Below Water), directly and indirectly affect five other SDGs through their interactions with microalgae. This study thus underscores the complex and interconnected nature of MPs pollution at environmentally relevant concentrations and their impacts on ecological and bioeconomic aspects of SDGs, thereby highlighting the urgent need for additional research and effective mitigation strategies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Research with the theory of planned behavior (TPB) has typically treated attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) as independent predictors of intention (INT). ...However, theoretically, PBC moderates the effects of ATT and SN on intention. In three studies dealing with different behaviors (voting, reducing household waste, and energy consumption) we show that greater PBC tends to strengthen the relative importance of ATT in the prediction of intention, whereas it tends to weaken the relative importance of SN. The latter pattern was observed in relation to injunctive as well as descriptive subjective norms, and it may help explain the relatively weak relation between SN and INT frequently observed in TPB studies.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Although many studies investigate the association between land use and station ridership, few examine their nonlinear and moderating relationships. Using metro smartcard data in Shenzhen, we develop ...a gradient boosting decision trees model to estimate the relative importance of land use variables and their threshold and moderating effects on ridership. We found that station betweenness centrality has the largest predictive power, followed by employment density and commercial floor area ratio (FAR). Results suggest that employment density, commercial FAR, and aggregate residential density should be set at 40,000 jobs/km2, 2, and 77,000 persons/km2, respectively, for maximizing ridership. The moderating effects show that population densification is more effective at terminal stations, whereas the policies intensifying nonresidential use work better at middle stations. These findings help planners prioritize land use strategies, identify effective ranges of land use metrics, and propose land use guidelines adaptive to the network position of stations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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