Does the extended producer responsibility system promote the green technological innovation of enterprises? An empirical study based on the difference-in-differences model

This paper aimed to accurately assess the driving effect of digital inclusive finance (DIF) on green economic growth, better implement DIF-related policies, and promote the development of green economy. Based on the urban panel data from 2011 to 2018 and the DIF index, this paper investigates the impact of DIF on economic green development and its internal mechanism. The results show that there is a significant positive U-shaped nonlinear relationship between DIF and green development. Through the robustness test of the threshold model, instrumental variable model (IV), and system generalized method of moments (SYS-GMM) model, the results are still valid. The mechanism research shows that the DIF can indirectly promote China’s green development by the coagglomeration degree of producer services and optimize and upgrade industrial structure. This study provides policy implications for developing countries around the world to achieve green development by promoting the DIF level.

Section: Introductionmentioning

confidence: 99%

Will Digital Inclusive Finance Make Economic Development Greener? Evidence From China

Fang

Liu

2021

“…analyzed the promotion effect of China's sustainable public procurement policy on science and technology innovation from the perspective of policymaking frequency and policymaking institutions, they pointed out that policymaking requires the participation of multiple institutions so that can form policy combinations, otherwise, the effectiveness of policy cannot be fully exploited. Zhao et al (2021) noted that the role of government subsidy mechanism on green technology innovation differs among enterprises of different types. In addition, studies have shown that policy combination plays a significant role in guiding and accelerating the innovation of lowcarbon solutions (Rogge and Schleich, 2018;Rafael et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

How Does Government Policy Improve Green Technology Innovation: An Empirical Study in China

Niu

et al. 2022

This paper divides government policy according to policy quantity, policy effectiveness and policy executive force so that the government policy can be quantified in more detail. Green patent data is used to represent green technological innovation, and the fixed effect model and panel data analysis from 2010 to 2019 are employed. The empirical results show that government policy has a significant direct promoting effect on green technology innovation. And the positive impact of policy quantity and policy effectiveness on green technology innovation is greater than that of policy executive force. In addition, the government policy will weaken the positive effect of enterprise innovation vitality on green technology innovation. Research conclusions also show that the direct and indirect effects of government policies on green technology innovation are heterogeneous. The government still needs appropriately policies adapted to the local situation, coordinated in policy quantity, policy effectiveness, and executive force, and accelerate the establishment of market-oriented green technology innovation environment. Different regions also should find the right green technology innovation policy scheme for their own regions.

“…As a result, researchers and policy makers are pushing and focusing on advance energy-efficient agricultural resource utilization, which would ultimately lead to environmental, social, and financial sustainability (Alluvione et al, 2011;Elahi et al, 2019a;Tayyab et al, 2020;Shah et al, 2021). The accelerating process of agricultural modernization increased the quantity of fossil fuel consumption; therefore, optimization is a way to reduce fossil energy and greenhouse gas (GHG) emissions (Rokicki et al, 2021;Zhao et al, 2021). The increased consumption of energy not only intensifies the environmental pollution but also brings serious threat to human life (Gu et al, 2019;Gu et al, 2020a;Gu et al, 2020b;Niu et al, 2020;Mir et al, 2021;Sepehri et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Energy Input–Output of Farms and Assessment of Greenhouse Gas Emissions: A Case Study of Cotton Growers

Abbas

Zhao

Waseem

et al. 2022

The concept of agricultural and environmental sustainability refers to minimizing the degradation of natural resources while increasing crop productions; assessment of inflow and outflow energy resources is helpful in highlighting the resilience of the system and maintaining its productivity. In this regard, the current study evaluated the amount of energy input–output of cotton productions and their environmental interventions. Data are randomly collected from 400 cotton farmers through face-to-face interview. Results suggested that the major energy is consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water (11,532.60, 11,121.54, and 4,531.97 MJ ha−1, respectively). Total greenhouse gas (GHG) emission is 1,106.12 kg CO2eq ha−1 with the main share coming from diesel fuel, machinery, and irrigation water. Stimulating data of energies, e.g., energy use efficiency (1.53), specific energy (7.69 MJ kg−1), energy productivity (0.13 kg MJ−1), and net energy gained (16,409.77 MJ ha−1). Further analysis using data envelopment analysis (DEA) showed that low technical efficiency, i.e., 69.02%, is the most probable cause of poor energy use efficiency. The impermanent trend in growth of energy efficiency has been witnessed with plausible potential of energy savings from 4,048.012 to 16,194.77 MJ ha−1 and a reduction of 148.96–595.96 kg CO2eq ha−1 in GHG emission. Cobb–Douglas production function is further applied to discover the associations of energy input to output, which inferred that chemical fertilizer, diesel fuel, machinery, and biocides have significant effect on cotton yield. The marginal physical productivity (MPP) values obliged that the additional use in energy (1 MJ) from fuel (diesel), biocides, and machinery can enhance cotton yield at the rate of 0.35, 1.52, and 0.45 kg ha−1, respectively. Energy saving best links with energy sharing data, i.e., 55.66% (direct), 44.34% (indirect), 21.05% (renewable), and 78.95% (nonrenewable), further unveiled the high usage of nonrenewable energy resources (fossil fuels) that ultimately contributes to high emissions of GHGs. We hope that these findings could help in the management of energy budget that we believe will reduce the high emissions of GHGs.