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Air quality is a social, economical, and health issue for fast-developing countries such as China. Due to the overuse of nonrenewable energy, industrialization, and the population put pressure on air quality, which seriously threatens public health and economic growth. This study focuses on air quality and also aims to investigate the short-and long-run correlation between foreign direct investment, energy consumption, domestic credit, and financial development. The Autoregressive distributed lag model and the Granger non-causality test were carried out over the period from 1985 to 2018. The main findings of this study show a positive and significant long-run impact of energy consumption on air quality. In addition, domestic credit and financial development similarly show a significant positive short-run association with air quality. Moreover, the unidirectional causality correlation running from foreign direct investment and domestic credit to air quality was concluded by the Granger non-causality test. Considering the empirical analysis, this study suggests that domestic financial institutions should offer credit to industries at a low-interest rate in order to help them to switch from non-renewable to renewable energy consumption towards the promotion of sustainable and healthy air quality.

Pakistan is an agricultural country where cereal crops are used as a staple food, but with time trend cereal production is decreasing. Therefore, this study aims to investigate asymmetric causality between agricultural carbon emissions, energy consumption, fertilizer consumption, and cereal food production in Pakistan. The secondary time series data over the period from1976 to 2018 was used to estimate the nonlinear-autoregressive distributed lag model. The empirical results of the linear Granger causality test confirm that the causality is running from energy consumption and fertilizer to cereal food production. The nonlinear Granger causality test declares cereal food production Granger cause to agricultural carbon emissions and energy consumption. It also confirms the unidirectional causality running from fertilizer consumption to cereal food production. Furthermore, the results of the nonlinear-autoregressive distributed lag model disclose that the positive and negative change in agricultural carbon emission, energy consumption, and fertilizer causes to changes in cereal food production. The dynamic multiplier curve suggests that positive and negative shocks influence cereal food production. The stability of the model was confirmed by the nonlinear-autoregressive distributed lag cumulative sum and cumulative sum of square test. Therefore this study suggests that it is essential for Pakistani farmers to switch from chemical fertilizer, burning non-renewable energy to organic fertilizer, and renewable energy in order to reduce carbon emission and increase cereal food production with a healthy environment.

This study examines the quantitative effects of market price in cotton producing areas of China. It also analyzes the qualitative effects of price regulation on cotton production. Secondary time series data were collected from National Bureau of Statistics in China, between 1990 and 2013. Calculation of the growth rate of cotton in production was done using linear trend model and multi-regression model to analyze the correlations between production, area and market price. The results of regression between the dependent variable (cotton production) and independent variables such as the previous year’s area, current year’s area and the previous year’s market price showed that the R-Square and adjusted R-Square values are 0.89 and 0.87, respectively, and the t-statistics of all independent variables rejected null hypothesis of no correlation at the 1% significant level. This infers that the market price and cultivating area of cotton crop has a highly significant relationship with the production. What’s more, the coefficients of current year’s area and previous year’s market price are higher than 0, denoting a positive impact on production. However, coefficient of previous year’s area is smaller than 0, implicating a negative influence on cotton production. In addition, the value of dry weight calculated is 1.94, which means no series auto-correlation exists. Despite this, it can be concluded from the regression results is that cultivating area and market price have time lag impacts on cotton production. Furthermore, price regulation has indirect positive impacts on cotton production. Key words: Cotton productivity, growth rate model, multi-regression.