Water resources in the Chia lagoon in Malawi experience a possible threat to sustainability. Communities are seeking alternatives to improve water quality in the lagoon. This study quantified the communities’ willingness-to-pay (WTP) and their influencing factors while using contingent valuation (CV) techniques. A wide range of data collection procedures, including focus group discussions, key informant interviews, field observation, and CV survey, were employed. A sample of 300 households was randomly selected. The CV results showed that 57.4% of the households were willing to pay. The monthly individual aggregate WTP amount ranged from MK696.83 (US$0.95) to MK81697 (US$111.38), and on average MK7870.45 (US$10.73), generating aggregate annual values ranging from MK6, 689,568 (US$9126.29) to MK784, 294,080 (US$1,069,978), and on average MK75,556,320 (US$103,078) (ceteris paribus). Logistic regression model demonstrated a significant (p < 0.01 or p < 0.05) relationship between demographic (gender, age, literacy level), social-economic (land ownership, main agriculture water source, and income), and institutional (civic education and social network, extension, institutional trust, household socio trust) factors and WTP. The findings from this study provide significant clues for further research and baseline information for local government and communities in the development of more effective and holistic approaches for improving water quality in natural ecosystems.

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Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

This study employed a deductive research approach and a survey strategy to assess risk perception and its influencing factors among construction workers in Malawi. Three specific construction hazards and their associated risks were selected. The hazards were “working at height (WAH)” “manual handling of loads (MHL)” and “heavy workload or intense pressure to be more productive (HWP).” The study engaged multistage sampling of 376 subjects. Univariate analysis, factor analysis and multiple linear regressions were performed in order to determine the main influencing factors among the independent variables. The study established that workers were aware of risks posed by their work. The majority perceived the risk associated with WAH, MHL and HWP as very high (62.7%, =8.80 ± 1.95); (48.5%, =8.10 ± 2.38); (57.9%, =8.49 ± 2.22) respectively. The study identified six factors as variables that showed a significant effect on workers’ perception of risk (p < 0.05). These factors were: “dreaded factor,” “avoidability and controllability,” “expert knowledge,” “personal knowledge,” “education level,” and “age”. It is concluded that contractors in the Malawian construction industry should integrate analysis of behaviors and risk perception of the workers and other players to guide the identification of better health and safety interventions at their worksites.