Irina Harun

To meet the dual-carbon objective, China's economy needs to move from a carbon-intensive to a green economy. Green technology innovation and renewable energy development are key to promoting green economic growth. The study uses 2010–2022 provincial panel data from 30 Chinese provinces. The super-efficient SBM model measures the province's green economic growth index. A panel fixed-effects model is used to experimentally evaluate how green technology innovation and renewable energy development affect green economic growth. Green financing moderates and a panel threshold model tests the threshold effect of renewable energy development on green economic growth. The results demonstrate that (1) China's green economic growth is rising and has a "U" shape. (2) all selected variables significantly impact green economic growth. However, population density, government intervention, and industrial structure are inversely associated with GEG. Green economic growth is significantly boosted by the moderate role of green finance in the development of renewable energy and green technological innovation. (3) Renewable energy development has a single threshold non-linear effect on green economic growth, depending on its development level and green technological innovation. Considering China's current condition and research findings, this study examines the green energy transition to promote green economic growth and high-quality economic development. Meanwhile, puts forward some important policy recommendations.

Water hyacinth (WH) is notorious for causing severe environmental degradation and being an economic burden to manage. However, it offers substantial prospects if exploited, especially by rural communities. High temperatures, eutrophic conditions and other environmental factors promote the proliferation of the plant in regions where it has been introduced. Regarded as among the world’s worst invasive weeds, WH is nearly impossible to control and eradicate without an integrated approach and community participation. The effectiveness of control methods varies, yet sustained community involvement determines the long-term success of these methods. Reproducing rapidly, WH has the resource capacity to support a unique microeconomic ecosystem, incentivising WH control by generating sustainable income. The WH ecology, the socioeconomic impacts of its invasion and its various applications are reviewed, and revenue generation and cost-saving options are highlighted. A circular microeconomic model is proposed by integrating WH valorisation into the general limitations of a rural community. Empowering locals with opportunities and enticing them with potential economic gains can be a nudge towards a pro-environment behavioural change in managing WH. This would aid in upgrading local livelihoods and could foster resilience within the community in tackling both environmental problems and economic setbacks through the management of WH invasions.

Machine learning techniques have been successfully used to simulate and optimise bioprocesses. This study explores the feasibility to apply Gradient Boosting, an emerging Ensemble Learning algorithm, which combines weak learners to generate better predictions for bioprocess dynamic modelling and prediction. A thorough procedure was presented for Gradient Boosting based data-driven model construction. Different case studies were employed including fermentation and algal photo-production processes. Given that generating a large size of experimental data for model training is time consuming and challenging to many bioprocesses, this work launched a first investigation on the data efficiency of Gradient Boosting by comparing its predictive capability against the predominantly used artificial neural networks. By carrying out a series of experimental verifications over a broad spectrum of process operating conditions, this study concluded that Gradient Boosting may have several advantages in small experimental datasets and can outperform artificial neural networks for bioprocess predictive modelling, indicating its potential for future bioprocess digitalisation and optimisation.