Climate, Volume 9, Issue 2 (February 2021) – 21 articles

Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one. Full article

The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital. Full article

Ae. (Stegomyia) aegypti L. and Aedes (Stegomyia) albopictus Skuse mosquitoes are major arboviral disease vectors in human populations. Interspecific competition between these species shapes their distribution and hence the incidence of disease. While Ae. albopictus is considered a superior competitor for ecological resources and displaces its contender Ae. aegypti from most environments, the latter is able to persist with Ae. albopictus under particular environmental conditions, suggesting species occurrence cannot be explained by resource competition alone. The environment is an important determinant of species displacement or coexistence, although the factors underpinning its role remain little understood. In addition, it has been found that Ae. aegypti can be adapted to the environment across a local scale. Based on data from the Neotropical country of Panama, we present the hypothesis that local adaptation to the environment is critical in determining the persistence of Ae. aegypti in the face of its direct competitor Ae. albopictus. We show that although Ae. albopictus has displaced Ae. aegypti in some areas of Panama, both species coexist across many areas, including regions where Ae. aegypti appear to be locally adapted to dry climate conditions and less vegetated environments. Based on these findings, we describe a reciprocal transplant experiment to test our hypothesis, with findings expected to provide fundamental insights into the role of environmental variation in shaping the landscape of emerging arboviral disease. Full article

Myanmar is located in a tropical region where temperature rises very fast and hence is highly vulnerable to climate change. The high variability of the air temperature poses potential risks to the local community. Thus, the current study uses 42 synoptic meteorological stations to assess the spatiotemporal changes in air temperature over Myanmar during 1971–2013. The nonparametric sequential Mann-Kendall (SqMK), linear regression, empirical orthogonal function (EOF), Principal Component Analysis (PCA), and composite analysis were used to assess the long-term trends in maximum (Tmax) and minimum (Tmin) temperature series and their possible mechanism over the study region. The results indicate that the trend of Tmax has significantly increased at the rates of 90% in summer season, while the Tmin revealed a substantial positive trend in winter season time series with the magnitude of 30%, respectively. Moreover, during a rapid change of climate (1995–2013) we observed an air temperature increase of 0.7 °C. The spatial distributions of EOF revealed relatively warmer temperatures over the whole region except the south in the summer; however, a similar pattern can be seen for the rainy season and winter, implying warming in the central part and cooling in the northern and southern parts. Furthermore, the Indian Ocean Dipole (IOD) influence on air temperature over Myanmar is more prevalent than that of the El Niño Southern Oscillation (ENSO). The result implies that the positive phase of the IOD and negative phase of the Southern Oscillation Index (SOI; El Niño) events led to the higher temperature, resulting in intense climatic extremes (i.e., droughts and heatwaves) over the target region. Therefore, this study’s findings can help policymakers and decision-makers improve economic growth, agricultural production, ecology, water resource management, and preserving the natural habitat in the target region. Full article

Several solutions and city planning policies have emerged to promote climate change and sustainable cities. The Sharing Cities program has the ambition of contributing to climate change mitigation by improving urban mobility, energy efficiency in buildings and reducing carbon emissions by successfully engaging citizens and fostering local-level innovation. A Digital Social Market (DSM), named Sharing Lisboa, was developed in Lisbon, Portugal, supported by an application (APP), enabling the exchange of goods and services bringing citizens together to support a common cause: three schools competing during one academic year (2018/2019) to win a final prize with the engagement of school community and surrounding community. Sharing Lisboa aimed to promote behaviour change and the adoption of energy-saving behaviours such as cycling and walking with the support of local businesses. Participants earned points that reverted to the cause (school) they supported. A total of 1260 users was registered in the APP, collecting more than 850,000 points through approximately 17,000 transactions. This paper explores how the DSM has the potential to become a new city service promoting its sustainable development. Furthermore, it is crucial for this concept to reach economic viability through a business model that is both profitable and useful for the city, businesses and citizens, since investment will be required for infrastructure and management of such a market. Full article

In Korea, a greenhouse gas (GHG) environmental impact assessment (EIA) has been conducting since 2012, which sets the evaluation procedures and methods for GHG items during the EIA. However, the current EIA on GHG emissions can support wrong decision-making because the evaluation does not consider Scope 3 GHG emissions. Accordingly, this study proposed the life cycle EIA (LCEIA) method to identify changes in GHG emissions that need to be managed by considering Scope 3 GHG emissions in construction projects. The LCEIA method incorporates life cycle CO₂ (LCCO₂) including Scope 1, Scope, and Scope 3 GHG emissions using the concept of life cycle assessment (LCA) into the scoping step of the EIA process. The case study was conducted using existing EIA on GHG emission and LCEIA methodology for a development project in Gwangyang City. Scenario 1 is defined as an approach that calculates GHG emissions using the existing EIA method, and scenario 2 is also defined as a process using the LCEIA method. Results reveal that Scenario 2, including Scope 3 GHG emissions, had 46.4−51.2% more GHG emissions than Scenario 1. Sensitivity analysis for electricity and liquefied natural gas (LNG) density was also performed. Although the change in the carbon emission factor of electricity had a slightly sensitive effect on the research results, the LNG density was found to be less sensitive. This study believes the importance of switching to an EIA reflecting life cycle carbon dioxide (LCCO₂) to calculate the exact amount of GHG emissions for construction work. Full article

Exposure to bushfire smoke is associated with acute and chronic health effects such as respiratory and cardiovascular disease. Residential buildings are important places of refuge from bushfire smoke, however the air quality within these locations can become heavily polluted by smoke infiltration. Consequently, some residential buildings may offer limited protection from exposure to poor air quality, especially during extended smoke events. This paper evaluates the impact of bushfire smoke on indoor air quality within residential buildings and proposes strategies and guidance to reduce indoor levels of particulates and other pollutants. The paper explores the different monitoring techniques used to measure air pollutants and assesses the influence of the building envelope, filtration technologies, and portable air cleaners used to improve indoor air quality. The evaluation found that bushfire smoke can substantially increase the levels of pollutants within residential buildings. Notably, some studies reported indoor levels of PM_2.5 of approximately 500µg/m³ during bushfire smoke events. Many Australian homes are very leaky (i.e., >15 ACH) compared to those in countries such as the USA. Strategies such as improving the building envelope will help reduce smoke infiltration, however even in airtight homes pollutant levels will eventually increase over time. Therefore, the appropriate design, selection, and operation of household ventilation systems that include particle filtration will be critical to reduce indoor exposures during prolonged smoke events. Future studies of bushfire smoke intrusion in residences could also focus on filtration technologies that can remove gaseous pollutants. Full article

Climate variability can influence the dynamics of aquatic invasive alien plants (AIAPs) that exert tremendous pressure on aquatic systems, leading to loss of biodiversity, agricultural wealth, and ecosystem services. However, the magnitude of these impacts remains poorly known. The current study aims to analyse the long-term changes in the spatio-temporal distribution of AIAPs under the influence of climate variability in a heavily infested tank cascade system (TCS) in Sri Lanka. The changes in coverage of various features in the TCS were analysed using the supervised maximum likelihood classification of ten Landsat images over a 27-year period, from 1992 to 2019 using ENVI remote sensing software. The non-parametric Mann–Kendall trend test and Sen’s slope estimate were used to analyse the trend of annual rainfall and temperature. We observed a positive trend of temperature that was statistically significant (p value < 0.05) and a positive trend of rainfall that was not statistically significant (p values > 0.05) over the time period. Our results showed fluctuations in the distribution of AIAPs in the short term; however, the coverage of AIAPs showed an increasing trend in the study area over the longer term. Thus, this study suggests that the AIAPs are likely to increase under climate variability in the study area. Full article

Flood damage assessments provide critical information for flood hazard mitigation under changing climate conditions. Recent efforts to improve and systemise damage assessments have focused primarily on urban environments with few examples for primary industries such as dairy. This paper explores the adverse consequences of flooding on dairy farms in the Bay of Plenty region, New Zealand. Ex-tropical Cyclone Debbie in April 2017 caused prolonged riverine and surface water flooding on over 3500 hectares of dairy farmland. The event provided an opportunity to develop and apply a participatory approach for collecting information about on-farm flood damage, and both response and recovery actions implemented by dairy farmers. Semi-structured interviews and transect walks with farmers revealed a range of direct and indirect damages to production and capital assets, influenced by duration of inundation, silt deposition and seasonality. Results highlight the need to identify on-farm and off-farm asset interdependencies of dairy farm systems to estimate long-term socio-economic consequences at farm-level. Enhancing dairy farm flood resilience in a changing climate will rely on farm-level response and recovery plans, proactively supported by emergency management agencies, farm service suppliers and support agencies. Full article

Although researchers have investigated the impact of Indian Ocean Dipole (IOD) phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) IOD phases on chlorophyll a (Chl-a) concentrations as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increase) in small pelagic fish production in the EIO off Java. Full article

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study. Full article

There are numerous studies assessing the influence of individual sociological, political, and demographic factors on attitudes towards climate change. However, there is still a need for a deeper understanding of the reasons behind these attitudes and for research based on results from more than one country. This study empirically examines a range of psychosocial and demographic determinants of support for climate policy (renewable energy, energy efficiency and carbon tax) in Germany and Poland (n = 1969). The results show that the societies of both countries, despite significant differences in income, culture and political stance on climate change, similarly support implementation of climate policies. For both countries valid predictors of support are: awareness, emotional response to climate crisis, sense of control, and belief in effectiveness of solutions; the study also shows predictors relevant in only one country. Factor analysis identified similar dimensions of attitudes toward climate change in both countries. The main findings show that support for climate policy is high in both countries and that the public is ready to accept more ambitious climate goals. Despite the differences between the countries, a coherent climate policy seems justified. The study also shows differences between the countries and provides recommendations for policymakers. Full article

The study explores the potential changes in water year types and hydrological droughts as well as runoff, based on which the former two metrics are calculated in the Central Valley of California, United States, in the 21st century. The latest operative projections from four representative climate models under two greenhouse-gas emission scenarios are employed for this purpose. The study shows that the temporal distribution of annual runoff is expected to change in terms of shifting more volume to the wet season (October–March) from the snowmelt season (April–July). Increases in wet season runoff volume are more noticeable under the higher (versus lower) emission scenario, while decreases in snowmelt season runoff are generally more significant under the lower (versus higher) emission scenario. In comparison, changes in the water year types are more influenced by climate models rather than emission scenarios. When comparing two regions in the Central Valley, the rain-dominated Sacramento River region is projected to experience more wet years and less critical years than the snow-dominated San Joaquin River region due to their hydroclimatic and geographic differences. Hydrological droughts in the snowmelt season and wet season mostly exhibit upward and downward trends, respectively. However, the uncertainty in the direction of the trend on annual and multi-year scales tends to be climate-model dependent. Overall, this study highlights non-stationarity and long-term uncertainty in these study metrics. They need to be considered when developing adaptive water resources management strategies, some of which are discussed in the study. Full article

Wine producers face several challenges regarding climate change, which will affect this industry both in the present and the future. Vulnerability assessments are at the forefront of current climate research, therefore, the present paper has two main aims. First, to assess two components of climate vulnerability regarding the Szekszárd wine region, Hungary; second, to collect and analyze adaptation farming techniques in terms of environmental sustainability aspects. Exposure analyses revealed that the study area will face several challenges regarding intensive drought periods in the future. Sensitivity indicators show the climate-related characteristics of the most popular grapevines and their relatively high level of susceptibility regarding changing climatic patterns. Since both external and intrinsic factors of vulnerability show deteriorating trends, the development of adaptation actions is needed. Adaptation interventions often provide unsustainable solutions or entail maladaptation issues, therefore, an environmental-focused sustainability assessment of collected interventions was performed to avoid long-term negative path dependencies. The applied evaluation methodology pointed out that nature-based adaptation actions are preferred in comparison to using additional machines or resource-intensive solutions. This study can fill the scientific gap by analyzing this wine region for the first time, via performing an ex-ante lock-in analysis of available and widely used adaptation interventions in the viticulture sector. Full article

This work illustrates the contribution of flood risk assessment and adaptation to set up a conservation management plan for a masterpiece of 20th-century architecture. Case study is the iconic complex, internationally known as the National Art Schools of Cuba. It consists of five buildings built in the early 1960s within a park of Habana next to the Caribbean Sea. The path of the river (Rio Quibù) crossing the estate was modified to fit the landscape design. The complex has then been exposed to the risk of flooding. The School of Ballet, located in a narrow meander of the river, slightly upstream of a bridge and partially obstructing the flow, is particularly subject to frequent flash floods from the Rio Quibù, and it needs urgent restoration. Keeping ISA Modern is a project aimed at preserving the Schools complex. Based upon in situ surveys on the Rio Quibù and local area measurements during 2019, numerical modelling, and previous work by the Cuban National Institute of Hydraulic Resources, we pursued a flood risk analysis for the area, and a preliminary analysis of available risk reduction strategies. Using HEC-RAS 2D software for hydraulic modelling, we evaluated the flooded area and the hydraulic conditions (flow depth, velocity) for floods with given return periods. Our results show that SB is a building most subject to flooding, with high levels of risk. Defense strategies as designed by Cuban authorities may include a (new) wall around the School of Ballet and widening of the river channel, with high impact and cost, although not definitive. Temporary, light, permanent, and low cost/impact flood proofing structures may be used with similar effectiveness. We demonstrate that relatively little expensive hydraulic investigation may aid flood modelling and risk assessment in support of conservation projects for historically valuable sites. This may support brainstorming and the selection of (low to high cost) adaptation and risk reduction measures in the coastal areas of Cuba in response to ever increasing extreme storms and sea level rise controlling flood dynamics under transient climate change. Full article

Short-rotation woody crops have maintained global prominence as biomass feedstocks for bioenergy, in part due to their fast growth and coppicing ability. However, the water usage efficiency of some woody biomass crops suggests potential adverse hydrological impacts. Monitoring tree water use in large-scale plantations would be very time-consuming and cost-prohibitive because it would typically require the installation and maintenance of sap flux sensors and dataloggers or other instruments. We developed a model to estimate the sap flux of eastern cottonwood (Populus deltoides. Bartr. ex Marsh.)) grown in bioenergy plantations. This model is based on adjusted vapor pressure deficit (VPD) using Structural Thinking and Experiential Learning Laboratory with Animation (STELLA) software (Architect Version 1.8.2), and is validated using the sap flux data collected from a 4-year-old eastern cottonwood biomass production plantation. With R² values greater than 0.79 and Nash Sutcliffe coefficients greater than 0.69 and p values < 0.001, a strong agreement was obtained between measured and predicted diurnal sap flux patterns and annual sap flux cycles. We further validated the model using eastern cottonwood sap flux data from Aiken, South Carolina, USA with a good agreement between method predictions and field measurements. The model was able to predict a typical diurnal pattern, with sap flux density increasing during the day and decreasing at night for a 5-year-old cottonwood plantation. We found that a 10% increase in VPD due to climate change increased the sap flux of eastern cottonwood by about 5%. Our model also forecasted annual sap flux characteristics of measured cycles that increased in the spring, reached a maximum in the summer, and decreased in the fall. The model developed here can be adapted to estimate sap flux of other trees species in a time- and cost-effective manner. Full article

Climate change is a significant challenge for policy makers, planners and communities. While adaptation responses are generally recognised to be place-based, policy processes on adaptation often reside with central (state or national) governments that may be remote from regional communities. In this paper, we contribute to the literature regarding how diverse regional communities engage with planning and policy for climate adaptation, which is important for successful implementation. We adopt a social network analysis (SNA) approach that enables an exploration of the interaction of community networks with policy information. There are limited empirical studies of information sharing about climate adaptation policy through community knowledge networks. One previous study, located in coastal New South Wales, Australia, mapped the community’s knowledge acquisition and diffusion to reveal the underlying network structures that influenced policy engagement pathways. However, further studies are needed to determine how the features of community networks may change with local context (e.g., coastal versus inland). This paper extends previous studies to compare and contrast adaptation knowledge networks in three NSW communities: Shoalhaven (the original coastal study site), Bega (coastal) and Orange (inland). Findings suggest that the presence of a natural resource-dependent industry, local geographies and boundary spanners acting as network knowledge brokers are factors influencing community knowledge flows. The work further demonstrates the utility of SNA to measure knowledge networks that can inform government engagement and communication with communities on climate adaptation policy. Full article

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture. Full article

Besides a proper agronomic management followed by Nepalese farmers, wheat (Triticum aestivum L.) production has been severely affected by changing climate. There are many interventions, including climate-smart practices, to cope with this situation and possibly enhance crop and soil productivity. Field experiments were set up in a randomized complete block design with six treatments (TRT) with four replications in three locations (LOC) during wheat-growing seasons in Nepal from 2014 to 2016. Treatments included (i) Controlled Practice (CP), (ii) Improved Low (IL), (iii) Improved High (IH), (iv) Climate Smart Agriculture Low (CSAL), (v) Climate Smart Agriculture Medium (CSAM), and (vi) Climate Smart Agriculture High (CSAH), whereas those LOC were Banke, Rupandehi and Morang districts. There was a significant main effect of TRT and LOC on grain yield and a significant interactionn effect of TRT × LOC on biomass yield in 2014–2015. About 55.5% additional grain yield was produced from CSAM treatment compared to CP in 2014–2015. Among locations, grain yield was the highest in Banke (3772.35 kg ha⁻¹) followed by Rupandehi (2504.47 kg ha⁻¹) and Morang districts (2504.47 kg ha⁻¹). In 2015–2016, there was a significant interaction effect of TRT × LOC on grain and biomass yields. The highest grain yield was produced from CSAH treatment in Banke district in 2015–2016. Overall, grain yield and other parameters showed a better response with either of the climate-smart interventions (mostly CSAH or CSAM) despite variability in geography, climate, and other environmental factors indicating the potential of climate-smart practices to improve wheat production in southern plains of Nepal. Full article

Existing survey instruments of trust in science and scientists that focus on the general public are potentially insufficient to assess climate skeptics’ perspectives towards climate science. They may miss important aspects of climate science about which skeptics raise concerns, and may not accurately measure climate skeptics’ distrust in climatology. We introduce a new survey instrument developed using data gathered from interviewing 33 self-identified climate change skeptics in Idaho. The survey items capture skeptics’ beliefs regarding climate scientists’ trustworthiness and credibility, their deference to scientific authority, and their perceptions of alienation from the climate science community. We validate our survey instrument using data from an online survey administered to 1000 residents in the U.S. Pacific Northwest who are skeptical of climate change. By employing standard survey design principles, we demonstrate how our new (dis)trust in climate science instrument performs in tandem with well-known predictors of science attitudes and pro-environmentalism. Full article