Naoko Kaida

Pro-environmental behavior correlates with present and future subjective well-being

This paper reports structural associations between psychological states, pro-environmental behavior, and present and future subjective well-being. A mailed questionnaire surveyed residents in Stockholm County, Sweden, to gather data on pro-environmental behaviors in water and energy saving at home, universalism and intrinsic satisfaction as motivational factors, and present and future subjective well-being. Results of path analysis suggest that: (1) Psychological factors (i.e., universalism, frugality, and participation categories of intrinsic satisfaction) correlate with pro-environmental behavior; (2) pro-environmental behavior could enhance not only present subjective well-being but expectations of future subjective well-being; and (3) expectations of future subjective well-being are negatively associated with current pro-environmental behavior. An important implication of this study is that expectation of better future subjective well-being may not always facilitate pro-environmental behavior. These results suggest that psychological factors both as antecedents and consequences play a significant role in building a sustainable society and improving our quality of life.

Spillover effect of congestion charging on pro-environmental behavior

Abstract This paper reports an examination of a spillover effect from a real-world intervention policy of traffic congestion charging that impacted various pro-environmental behaviors. A mailed questionnaire surveyed randomly sampled car owners living in Stockholm County (Sweden) to gather data on travel modes and pro-environmental behavior before and after the introduction of a trial congestion charging policy in Stockholm. Results of paired t test on 291 valid samples revealed that this policy not only directly mitigates traffic congestion (i.e., a direct effect of the policy intervention), but it also indirectly ‘spills over’ to affect pro-environmental behaviors in nontransportation situations (resource and energy use). A spillover to pro-environmental behavior was confirmed by respondents who shifted their travel mode from car to pro-environmental travel mode. Interestingly, the spillover was also confirmed in a group of respondents who remained in the pro-environmental travel mode and others who adversely shifted away from a pro-environmental travel mode to cars. Results suggest that the spillover effect from an environmental policy intervention can have considerable impact on facilitating pro-environmental behaviors and surrounding issues in more general contexts, thus warranting careful evaluation with a wider perspective.

Monitoring Mangrove Forest Changes in Cat Ba Biosphere Reserve Using ALOS PALSAR Imagery and a GIS-Based Support Vector Machine Algorithm

Cat Ba is one of the most well-known islands located in North Vietnam, which has been recognized as a biosphere reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2004. Despite the large potential carbon stocks in mangrove forests of Cat Ba, the mangrove ecosystem of this island has suffered severe deforestation and forest degradation due to the conversion to shrimp aquaculture. Monitoring mangrove forest changes plays an important role for effective mangrove conservation and management. The objectives of this study were to map the spatial distribution of mangrove forest and to assess their changes between 2010 and 2015 in Cat Ba Biosphere Reserve, Hai Phong city of Vietnam using ALOS PALSAR data and a GIS-based support vector machine algorithm. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected. Then, spatial distributions of mangroves were derived using the support vector machine classifier. The results showed that the ALOS-2 PALSAR for 2015 achieves the overall accuracy of 85% and the kappa coefficient of 0.81, compared with those of 81% and 0.77, respectively from the ALOS PALSAR for 2010. The mangrove forest areas in the Cat Ba Biosphere Reserve, Vietnam decreased by 15% from 2010 to 2015. This research shows the potential use of ALOS PALSAR data combined with machine learning techniques in monitoring mangrove forest changes in tropical and semi-tropical climates.