Climate change has become a significant concern for developing countries given the risk that it poses to energy and food independence, and to general productivity. Despite having an energy system with low carbon intensity when compared to other Latin American countries, Colombia is already facing climate change impacts and requires urgent efforts to mitigate them. As a developing country, the challenge is bigger as policies for economic growth should be in line with the global commitment of reducing greenhouse gas emissions. With the aim of contributing to the design of climate policies by using data driven methodologies and scientific evidence, this study assesses the impact of economic development on the environment by examining the validity of the environmental Kuznets curve hypothesis for Colombia. Statistically validated and stable autoregressive distributed lag models are estimated for three different environmental indicators: carbon dioxide emissions, methane emissions, and the ecological footprint. Moreover, the effects of other variables such as urbanization, foreign direct investment, the value added by the agricultural and industrial sectors, and energy use are analyzed with dynamic simulations. To the best of our knowledge, there are no reports of EKC estimation using these variables in statistically validated models for Colombia; thus, this paper adds to the scientific evidence of the EKC hypothesis in developing countries. Empirical evidence supports a long-run equilibrium relationship among investigated variables and the existence of an inverted U-shaped EKC relationship between gross domestic product (GDP) and methane emissions, and GDP and the ecological footprint. The results of this paper show that shifting to renewable energy sources and leveraging the use of cleaner technologies in agricultural and industrial sectors are key factors for economic growth without harming the environment, which has important scientific value because of its evidence-based approach for public policy making in developing countries, particularly Colombia.

The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical, oil and gas industries, especially during transportation. There are two main approaches to solve this problem in literature: data based algorithms and mechanistic models. Although data based methods may achieve better prediction accuracy, they fail to explain the two-phase characteristics (i.e. pressure gradient, holdup, gas and liquid local velocities, etc.). Recently, many approaches have been made for establishing a unified mechanistic model for steady-state two-phase flow to predict accurately the mentioned properties. This paper proposes a novel data-driven methodology for selecting closure relationships from the models included in the unified model. A decision tree based model is built based on a data driven methodology developed from a 27670 points data set and later tested for flow pattern prediction in a set made of 9224 observations. The closure relationship selection model achieved high accuracy in classifying flow regimes for a wide range of two-phase flow conditions. Intermittent flow registering the highest accuracy (86.32%) and annular flow the lowest (49.11%). The results show that less than 10% of global accuracy is lost compared to direct data based algorithms, which is explained by the worse performance presented for atypical values and zones close to boundaries between flow patterns.

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.