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Article

Integrating the IoT and New Energy to Promote a Sustainable Low-Carbon Economy

by
Yan Chen
,
Yuqi Hou
* and
Jiayi Lyu
School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6755; https://doi.org/10.3390/su17156755
Submission received: 3 June 2025 / Revised: 18 July 2025 / Accepted: 21 July 2025 / Published: 24 July 2025
(This article belongs to the Special Issue Advances in Low-Carbon Economy Towards Sustainability)
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Abstract

This study explores the complex interaction between the Internet of Things (IoT) and the new energy sector and analyzes how their integration can catalyze a transition toward a sustainable low-carbon economy. Through the full-sample and rolling sub-sample methods, we empirically examine the dynamic interrelationship between China’s IoT index (IoT) and the New Energy Index (NEI). Quantitative analysis reveals significant time-varying characteristics and bidirectional causal complexity in the interaction between the IoT and new energy. The IoT has a dual-edged impact on the development of new sources of energy. In the long run, the IoT plays a dominant role in incentivizing new energy, helping to enhance its stability and economic value. However, during stages characterized by technological bottlenecks or resource competition, the high energy consumption of IoT infrastructure may suppress the investment returns of new energy. Simultaneously, new energy has both positive and negative impacts on the IoT. On the one hand, new energy provides low-cost, sustainable power to support the IoT, driving the construction of the IoT ecosystem. On the other hand, it may threaten the continuity of IoT power supply, and the complexity of standardization and regulation in the sector may constrain the development of the IoT. This study provides a fresh perspective on promoting the integration of digital technology and green energy, uncovering nonlinear trade-offs between innovation-driven growth and carbon reduction goals, and offering policy insights for cross-sectoral collaboration to achieve sustainability.

1. Introduction

This research explores the evolving interplay between the IoT and new energy technologies in the context of global energy transformation, offering insights to guide the development of sustainable low-carbon pathways. With the rapid development of the global economy and ongoing urbanization and industrialization, global energy demand continues to rise, leading to increased uncertainty and complexity in the energy market [1]. Against this backdrop, the global energy landscape is undergoing profound changes. Although traditional energy sources remain abundant in many regions, concerns over environmental impact, energy security, and the need for sustainable development have become increasingly prominent, leading to a global shift toward cleaner energy alternatives [2,3]. The significance of new energy sources, such as solar, wind, and biomass energy, within the global energy structure is growing steadily. The focus on developing and exploring new sources of energy to replace overexploited traditional ones has become a common direction for resource utilization among all countries [4]. In the field of new energy development, advanced technologies have emerged as the core engine driving the upgrade of the energy industry and low-carbon energy transitions [5]. Particularly, IoT technology is reshaping the operational paradigms of new energy systems through its comprehensive digital interconnectivity and intelligent regulation capabilities [6]. The IoT refers to a network infrastructure that connects physical objects to the internet through information sensing devices and agreed communication protocols. This enables the exchange and transmission of data between objects and networks, thereby facilitating intelligent identification, positioning, tracking, monitoring, and management. The implementation of IoT-driven predictive maintenance and adaptive load balancing mechanisms further facilitates dynamic energy structure optimization, accelerating the global transition to low-carbon energy systems. These technological advancements not only enhance energy resilience against supply demand fluctuations but also serve as critical enablers for achieving the United Nations’ Sustainable Development Goals (SDGs) related to affordable clean energy [7]. However, this digital transformation also brings a significant sustainability challenge. The rapidly expanding IoT infrastructure generates massive computational demands and consumes substantial energy, which may undermine its intended environmental benefits. Therefore, integrating the IoT with new energy sources is not only beneficial but essential for building scalable, sustainable, and resilient energy systems. Understanding the dynamic interaction between these two domains is thus of great practical and policy importance for advancing global sustainability.
According to the White Paper on the Intelligently Interconnected Digital Economy released at the 2024 World Internet of Things Convention (WIOTC), the number of IoT connections worldwide has reached approximately 25 billion, while IoT infrastructure investments now account for over 60% of global IoT-related infrastructure expenditures. As the scale of the industry continues to expand, the energy challenges confronting the development of IoT technologies have become increasingly prominent [8]. The massive computational demands and real-time data processing requirements generated by vast numbers of connected devices have led to exponential growth in the energy consumption system, requiring the integration of new energy solutions to support green and sustainable development goals [9]. Against this backdrop, the IoT and renewable energy are increasingly converging in a synergistic development pattern, jointly constructing a new infrastructure system characterized by the deep integration of digitalization and decarbonization. Although the extant literature has explored the relationship between the IoT and new energy, most studies tend to focus on one-way effects, lacking a comprehensive and dynamic analytical framework [10,11]. Therefore, it is necessary to adopt a more integrated approach to advance current research.
The integration of the IoT and new energy technologies is emerging as a key driver of the global transition toward a low-carbon, digital economy. On one hand, the IoT enables real-time data collection, intelligent optimization, and efficient energy management, helping to reduce carbon emissions and improve the performance of renewable energy systems [12]. On the other hand, the digital transformation itself brings new sustainability challenges. The rapid expansion of IoT infrastructure demands massive computational power and consumes significant amounts of energy, potentially offsetting its environmental benefits [13]. This paradox highlights a crucial issue: integrating the IoT with new energy is not merely beneficial but necessary for building scalable, sustainable, and resilient energy systems [14,15]. Understanding the evolving interaction between these two sectors is therefore essential, both in theory and in practice, for shaping effective strategies to support green innovation and global sustainability [16].
Against this background, this study aims to explore the dynamic and bidirectional relationship between the development of the IoT and the performance of the new energy industry, as represented by the NEI. While the existing literature has largely emphasized the technological and environmental benefits of the IoT, limited attention has been paid to its role in shaping the evolution of the new energy sector and vice versa. This paper fills this gap by empirically examining not only whether IoT development significantly affects NEI performance over time but also how the NEI, in turn, influences the progress of IoT technologies. The interplay between the IoT and NEI is conceptualized as nonlinear and time-varying. On the one hand, IoT development can promote the new energy sector by improving energy efficiency, enabling intelligent grid management, and facilitating the integration of renewable sources. However, it can also constrain the sector, especially during periods of high energy demand caused by digital infrastructure bottlenecks. Conversely, the advancement of the new energy industry may support the sustainability of the IoT ecosystem by providing low-cost, clean electricity, yet it may also create new challenges such as intermittency in energy supply or increased regulatory complexity. We therefore hypothesize that the dominant direction and nature of this interaction—whether synergistic or competitive—vary significantly over time, depending on factors such as technological maturity, resource availability, and policy regimes. To test this proposition, we adopt a time series framework capable of capturing dynamic dependencies, structural breaks, and long-run equilibrium mechanisms between the IoT and NEI.
This study aims to investigate the dynamic and bidirectional relationship between the IoT and new energy development, particularly in the context of promoting a sustainable low-carbon economy in developing countries. It contributes to the literature in several novel and significant ways. Firstly, the prior literature has primarily focused on the unidirectional impact of the IoT on energy systems [13,17] while systematically overlooking the bidirectional causality mechanisms between these two domains. By recognizing that distributed IoT networks can facilitate energy integration, and that renewable energy can in turn power IoT infrastructures, we introduce a dual-perspective framework that reveals a mutually reinforcing symbiotic relationship between the two sectors. This dynamic interaction plays a vital role in enabling real-time energy management, improving energy efficiency, and reducing carbon emissions. Secondly, the existing literature on technology and energy has predominantly focused on developed countries [5], with limited attention given to developing economies. However, understanding the dual causal relationship between the IoT and renewable energy is crucial for addressing the tension between economic growth and environmental protection in developing countries. As the largest developing country, China exemplifies a representative case, and the insights drawn from its data offer valuable implications for promoting sustainable development in similar contexts. Thirdly, this study underscores the evaluation of new energy by introducing a comprehensive assessment framework. Existing evaluations predominantly concentrate on isolated energy sources [18,19], overlooking the interconnectedness within the energy ecosystem. In contrast, our research devises a new energy composite index, thereby presenting a more comprehensive assessment of green energy development. Additionally, the IoT index is utilized to gauge progress in IoT technology. By analyzing quarterly data from January 2011 to March 2023, this study explores the relationship between the IoT and NEI. Finally, this paper achieves a major breakthrough at the methodological level. Most studies have adopted a single full-sample analysis method [4], which makes it difficult to capture both long-term development trends and short-term dynamic changes simultaneously, and this method is also unable to accurately identify the complex causal mechanisms between the two. This paper not only conducts full-sample regression to grasp the overall development law, but it also analyzes the heterogeneity characteristics of different stages and regions through sub-sample regression, effectively compensating for the limitations of traditional research.
The structure of this paper is as follows: Section 1 provides an introduction, outlining the research background and objectives. Section 2 presents a literature review, summarizing previous studies related to the IoT and renewable energy. Section 3 and Section 4 focus on the methodology and data, introducing the empirical model and data sources. Section 5 offers quantitative analysis, including the model estimation results. Section 6 provides a comprehensive discussion, comparing the findings with the existing literature and addressing potential limitations. Finally, Section 7 concludes this study and provides relevant policy recommendations.

2. Literature Review

2.1. Positive Impacts of IoT on New Energy Systems

In the field of energy research, the IoT has emerged as a central topic in the study of renewable energy systems. The existing literature has primarily focused on the unidirectional influence of the IoT on energy systems. At the system integration level, the IoT enables the real-time collection and transmission of energy data across the entire supply chain. When combined with AI algorithms, it facilitates accurate forecasting and efficient energy dispatching [20]. Moreover, the IoT helps dismantle industry “information silos” and, through the application of machine learning, optimizes cross-sectoral energy coordination, enhances energy utilization efficiency [21,22], and stabilizes electricity output by leveraging diverse energy sources, thereby improving overall system performance [23].
In terms of efficiency enhancement, the IoT transforms the traditional high-energy-consuming and labor-intensive renewable energy production model. Smart factories equipped with IoT systems enable real-time fault detection, significantly reducing energy waste [24]. Through optimized device operation, IoT systems can lower overall energy consumption [25], while sensors allow for precise data collection and analysis to optimize energy allocation, reduce operational costs, and minimize consumption [26]. Real-time monitoring across the entire supply chain further reduces energy loss and CO2 emissions [10]. However, challenges remain—particularly the high energy consumption and electronic waste associated with IoT devices, which conflict with low-carbon goals [26].
Regarding system security, the IoT contributes significantly by enabling real-time monitoring and early warning, predictive maintenance, and improved emergency response capabilities [27]. Sensors continuously monitor renewable energy facilities and environmental conditions, allowing for the early detection of anomalies and timely alerts [28]. Predictive maintenance involves analyzing equipment data to construct fault prediction models and optimize repair schedules, thereby improving equipment reliability. During emergencies, the IoT enables rapid fault localization and the activation of backup energy supply systems, thereby mitigating disruptions [29]. These improvements not only enhance operational performance but also contribute to the long-term sustainability of renewable energy systems by minimizing energy waste and reducing CO2 emissions.

2.2. Risks and Limitations of IoT in New Energy Development

On the other hand, IoT integration in renewable energy systems also poses a number of challenges. At the level of energy management system construction, excessive reliance on digital platforms has led to increasing system complexity. High-frequency data interactions often cause algorithmic delays, which in turn exacerbate the lag in energy dispatching. Protocol compatibility issues between heterogeneous devices frequently result in instruction execution errors, directly undermining management efficiency [30]. Communication delays often lead to malfunctions in energy storage devices, especially in areas with weak network coverage, where frequent signal interruptions contribute to a high rate of dispatching errors and significantly impair the reliability of supply–demand balancing.
From the perspective of energy structure transition, although the IoT is widely regarded as a key enabler of energy transformation, its high energy consumption characteristics run counter to sustainable development goals [31]. A single IoT sensor consumes a considerable amount of energy annually, and on a large scale, this operational consumption can significantly offset the emission reduction benefits achieved by renewable energy [26]. Furthermore, the short lifecycle of IoT devices, coupled with low e-waste recycling rates, exacerbates environmental damage through heavy metal pollution near clean energy installations.
In terms of system security and resilience, while the IoT retrofitting of traditional power plants aims to enhance reliability [23], the vulnerability of sensor networks introduces new security risks. Cyberattacks can easily manipulate equipment data, triggering false alarms and cascading failures. If the data supporting predictive maintenance is tampered with, it may result in increased maintenance costs and resource waste. The extensive interconnectivity of devices not only enlarges the attack surface but also increases the risk of core data leakage, such as grid loads and energy storage status, which can be exploited maliciously, threatening the balance of energy dispatch and the stability of the low-carbon energy supply chain [32,33]. Although cross-industry information sharing improves coordination efficiency, it simultaneously raises the risk of data breaches. Unauthorized access to energy consumption data may lead to unfair competition or malicious attacks, further undermining the security and stability of renewable energy systems.

2.3. The Influence of New Energy Development on the IoT

Energy availability serves as a key constraint in the development of the IoT, affecting not only hardware power supply but also software design, system architecture, and the application ecosystem. Traditional power supply systems have inherent limitations. Battery-powered IoT devices often suffer from short lifespans and high replacement costs; for example, industrial sensors require periodic manual charging, which limits deployment flexibility. Overreliance on fossil fuels to power IoT devices contradicts the principles of carbon neutrality and exacerbates environmental pollution [34].
Against this backdrop, breakthroughs in renewable energy provide critical momentum for IoT advancement. With the aid of energy harvesting technologies—such as piezoelectric, thermoelectric, and photovoltaic effects—IoT devices can achieve self-powering. Moreover, renewable energy improves the feasibility of deploying the IoT in remote areas where traditional grid access is limited. In mountainous or marine regions, solar and wind energy can power IoT systems independently. For instance, marine monitoring buoys utilize wave energy to generate electricity and transmit hydrological data continuously without the need for manual maintenance [35].
Despite the sustainability advantages of renewable energy, its temporal and spatial intermittency presents significant challenges to the power reliability of IoT devices. For example, solar-powered sensors experience sharp declines in energy harvesting efficiency during overcast or rainy weather, leading to a mismatch between power supply and device energy consumption [36]. As a result, the net impact of renewable energy on IoT deployment and operation remains inconclusive, warranting further empirical investigation.

2.4. Summary and Research Gap

In summary, the existing literature has explored the impact of the IoT on renewable energy from various angles. However, there is no consensus on whether the effect is predominantly beneficial. While IoT applications heavily depend on reliable energy support, the development of renewable energy also affects the deployment and operation of IoT systems. Nonetheless, quantitative evidence on the influence of renewable energy on the IoT remains limited, and there is a significant gap in analyzing the bidirectional relationship between these two domains.
To address these gaps, this study constructs a bidirectional analytical framework between the IoT and NEI. It investigates not only the positive impact of the IoT on energy management and efficiency improvement but also the reverse effects of renewable energy development on IoT infrastructure. By applying a rolling window Granger causality approach, this study reveals the nonlinear and time-varying interaction mechanisms between the two sectors. This research fills an important void in the literature and offers valuable theoretical insights for the coordinated development of digital and energy systems.

3. Methodology

3.1. Full-Sample Technique

In the traditional Granger causality framework, the validity of the results relies on the assumption that the test statistics follow a standard asymptotic distribution, which in turn depends on the stationarity of the time series and the presence of large sample sizes [37,38]. When the data is non-stationary or the sample size is limited, the direct application of the Vector Autoregressive (VAR)-based Granger causality test may lead to inaccurate or misleading inferences [39]. To address these limitations, this study adopts the residual-based bootstrap (RB) method, which enhances the robustness of standard asymptotic tests. The modified likelihood ratio (LR) test incorporating the RB approach is particularly valuable for accurately evaluating relationships between variables when the underlying data deviates from normality [40]. Overall, this methodological framework offers a more robust tool for time series causality analysis. By improving the reliability of empirical inference, this approach facilitates a deeper understanding of the dynamic interactions between the IoT and NEI and contributes to both the theoretical development and the practical advancements in intelligent energy management.
Equation (1) specifies the bivariate VAR(p) framework employed for the RB-based modified LR causality analysis.
X t = β 0 + β 1 X t − 1 + ⋯ ⋯ β p X t − p + ε t   ,   t = 1 , 2 , ⋯ ⋯ , T
The selection of optimal lag length constitutes a crucial prerequisite in model specification, wherein the Akaike Information Criterion (AIC) operates as the key determinant. By minimizing the AIC value, we identify the lag order that balances model fit and complexity, which is subsequently operationalized through the composite vector Xt = (IoTt + NEI2t)′, thereby deriving the foundational Equation (2).
I o T t N E I 2 t = β 10 β 20 + β 11 ( L ) β 12 ( L ) β 21 ( L ) β 22 ( L ) I o T t N E I 2 t + ε 1 t ε 2 t
εt = (ε1t, ε2t)′ is a white noise process.
The lag polynomial is defined as β i j ( L ) = ∑ k = 1 p β i j , k L k , where i, j = 1, 2, and L is the lag operator such that L k X t = X t − k , for k = 1, 2, …, p. By testing the statistical significance of the coefficients β i j , k , we can determine the direction of Granger causality between the IoT and NEI.

3.2. Stability Test of Parameters

Full-sample analysis offers a general view of the long-term relationship between the IoT and NEI, and it implicitly assumes parameter stability over the entire period, which may overlook structural shifts or regime changes. In contrast, the rolling window approach allows for the dynamic estimation of the relationship over time, thereby capturing potential nonlinearities, breaks, or time-varying causality that the static full-sample model may miss. In the conventional full-sample Granger causality testing method, the validity of inference typically hinges on the assumption that parameters in the VAR model remain stable over the entire study period [41]. However, in real-world scenarios, evolving economic structures and shifting policy environments often lead to parameter instability, which undermines the reliability of the full-sample test results. Such instability not only reflects the time-varying nature of causal relationships between variables but may also render full-sample conclusions invalid due to potential structural breaks.
To address this issue, the Sup-f test can be employed to detect potential structural breaks within the model [42], while the Ave-f and Exp-F tests serve to evaluate parameter stability by aggregating temporal information across the entire sample period [43]. Furthermore, the Nyblom–Hansen Lc test (Lc test) developed by Nyblom is capable of identifying whether parameters follow a random walk process, thereby indicating time dependence [44]. This is particularly relevant in the context of variables like the IoT and NEI, where innovation-driven shocks and market fluctuations introduce pronounced time-varying dynamics. Relying solely on full-sample analysis risks masking these changes. Hence, this study adopts a rolling sub-sample framework, coupled with the aforementioned stability tests, to capture the evolving nonlinear interactions between the IoT and NEI more accurately.

3.3. Sub-Sample Technique

This study employs a rolling window sub-sample Granger causality testing approach to further investigate the causal relationship between IoT and NEI structural changes or non-stationarity [45]. The core of this method lies in progressively shifting a fixed-size window across the full sample. Given a window length of r, this approach generates T-r + 1 sub-samples. For each sub-sample, a modified likelihood ratio (LR) test combined with a bootstrap procedure is applied to assess time-varying causal linkages. The bootstrap method, conducted with Nb replications, generates the empirical distribution of the test statistic, providing estimates of average causal strength. The direction and magnitude of causality between the IoT and NEI are quantified using parameters from the corresponding VAR models.
We set the window size to 24 to adequately reflect the trade-off between estimation accuracy and local representativeness. Wider window lengths enhance parameter stability but may overlook temporal heterogeneity, whereas smaller windows improve sensitivity to short-term fluctuations at the cost of higher variance. This rolling window framework features dynamic segmentation, bootstrap refinement, and model-based quantification, which offers a flexible and robust tool for exploring causal dynamics in the presence of structural shifts or non-stationary time series.

4. Data

We use monthly data spanning from January 2011 to March 2025 to examine the dynamic relationship between the IoT and NEI in China. To capture the dynamics of these two sectors, we employ the IoT index (code: 884030.WI) and the New Energy Index (NEI, code: 884035.WI), both obtained from the WIND database. The IoT index is designed to reflect the overall market performance of listed companies involved in the IoT industry. It includes firms across several sub-sectors, such as IoT module manufacturers, terminal device producers, intelligent controller suppliers, and data service providers. The index adopts a market capitalization-weighted methodology to comprehensively represent sectoral trends. The NEI captures the performance of China’s new energy sector, encompassing a wide array of non-fossil energy sources including nuclear, solar, wind, biomass, geothermal, ocean, and hydrogen energy. The constituent companies primarily engage in new energy generation and equipment manufacturing.
Both indices are calculated based on relative price changes in constituent stocks. The real-time index value is derived using the following formula:
R e a l − T i m e   I n d e x P r e v i o u s   T r a d i n g   D a y   C l o s i n g   I n d e x = ∑ ( R e a l − T i m e   P r i c e   o f   C o n s t i t u e n t   S t o c k P r e v i o u s   C l o s i n g   P r i c e   o f   C o n s t i t u e n t   S t o c k ) N u m b e r   o f   L a t e s t   C o n s t i t u e n t   S t o c k s
The reason why 2011 is chosen as the starting point of this research is that in 2011, the German government proposed the term “Industry 4.0”, and the concept of the industrial Internet of Things re-entered people’s vision [46]. Concurrently, 2011 marked the beginning of China’s 12th Five-Year Plan, during which the Chinese government officially designated the IoT as a strategic emerging industry [47]. That same year, the Ministry of Industry and Information Technology issued the “12th Five-Year Plan for the Development of the Internet of Things”, signifying a significant elevation in the national prioritization of IoT development. Since 2011, driven by the market environment, policy environment, and international climate environment, China has formed a policy system to support the rapid development of new energy, thereby advancing its transition toward a sustainable low-carbon economy. In 2011, the National Energy Administration issued interim measures for wind power development and construction, while the National Development and Reform Commission (NDRC) released a notice on improving feed-in tariff policies for solar photovoltaic power.
Higher values of the IoT index and NEI indicate stronger development momentum in China’s IoT technology and new energy industries, respectively. Figure 1 illustrates the evolving trends in both indices, offering a preliminary insight into their interrelationship within the broader context of sustainable low-carbon development. From early 2011 to the end of 2012, both the IoT and NEI exhibited a slight downward trend. This period marked the early stages of development for both sectors, characterized by immature technologies and limited market penetration and a lack of effective integration between digital infrastructure and green energy systems. As a result, investors lacked sufficient information regarding the short-term returns of IoT technologies and the new energy industry. Following this adjustment phase, both sectors entered a period of rapid growth. Between early 2013 and May 2015, the IoT index surged from 838 to 4438, while the NEI rose from 580 to 1915. This surge reflected renewed momentum: the industrialization of the IoT gained significant traction in 2013, boosting market confidence. Simultaneously, the Chinese government’s strong policy support led to increased investment in the new energy sector, yielding initial signs of the successful deployment of a low-carbon economy. After this bullish phase, the market experienced notable turbulence. In June 2015, regulatory actions initiated by the China Securities Regulatory Commission (CSRC)—primarily targeting the crackdown on off-market margin financing—triggered severe stock market fluctuations, intensifying market panic. Although the government introduced a series of stabilization measures that led to a short-term recovery by December 2015, both indices entered a downward trajectory that persisted until October 2018. This phase is interpreted as an investment layout period, during which groundwork was laid for future returns in both industries.
Since October 2018, the IoT has shown an upward trend, but it is highly volatile. The application scenarios of the IoT continue to expand, but the development speed of different fields is different, and the uncertainty of market demand affects the drastic fluctuations in the IoT. In 2024, the convergence of artificial intelligence and IoT (AIoT) catalyzed breakthroughs in areas such as smart manufacturing and autonomous driving. The increasing prominence of the AIoT concept fueled renewed enthusiasm, propelling the IoT index upward to reach 3826 by the end of February 2025. Compared to the IoT sector, the development of the new energy industry has exhibited greater stability. From November 2018 to November 2021, the NEI maintained an upward trajectory, reaching its peak of 2510 in November 2021. However, in 2022, the global market entered a downward economic cycle, rendering high-valuation sectors such as new energy particularly sensitive to market fluctuations. Domestically, policy adjustments further influenced the sector. In December 2021, the Chinese government announced a reduction in subsidies for new energy vehicles, followed by the complete termination of the new energy vehicle purchase subsidy policy on 31 December 2022. Simultaneously, intensified competition and strategic bargaining across the upstream and downstream segments of the new energy industry chain contributed to increased uncertainty. As a result, the NEI began a sustained decline from December 2021 onward. Given the time-varying and complex interdependence between the IoT and new energy technologies, such relationships are difficult to capture using full-sample methods. To address this limitation, we use a sub-sample approach to more accurately explore the dynamic interactions between the IoT and NEI over time, thereby gaining deeper insights into their role in supporting a green, resilient, and sustainable economic transition.
Table 1 presents the descriptive statistics for the IoT index and NEI. The maximum and minimum values for the IoT index are 4457.731 and 651.610, respectively, while those for the NEI are 2510.484 and 479.726. The mean values are 2462.376 for the IoT index and 1176.198 for the NEI. In terms of dispersion, the standard deviation of the IoT index is 944.12—nearly twice that of the NEI (482.78)—indicating that the IoT index exhibits greater volatility and a more dispersed distribution. In terms of distributional characteristics, the IoT index exhibits left-skewness and platykurtosis, indicating a left-tailed and relatively flat distribution, potentially influenced by low-value outliers. The NEI shows right-skewness with a skewness coefficient of 0.854 and kurtosis close to normal, presenting a right-extended distribution with slightly thicker tails. The Jarque–Bera test further confirmed that both deviate from a normal distribution, with the NEI demonstrating more significant non-normality. Given the non-normal nature of the data, traditional Granger causality tests may yield unreliable results. Therefore, we rely on the RB-based revised LR technique and adopt a rolling window sub-sampling approach to analyze how the influence mechanisms between the IoT and NEI evolve over time.

5. Results and Analysis

We employ the Augmented Dickey–Fuller (ADF) test, Phillips–Perron (PP) test, and DF-GLS test to examine the stationarity of the IoT and NEI time series. As shown in Table 2, the results of the unit root tests indicate that both series become stationary after first differencing. Therefore, the first-order differences in the two variables are used in subsequent analyses. Based on the differenced data, the VAR model is then applied to examine the full-sample Granger causality relationship between the IoT and NEI.
The lag order is set to 1, and the number of bootstrap replications is set to 10,000. The full-sample Granger causality test results between the IoT and NEI are presented in Table 3. Based on the bootstrap-derived p-values, the null hypothesis H0 is rejected at the 10% significance level in the direction from the IoT to NEI, indicating that the IoT Granger causes the NEI. However, the reverse causality from the NEI to IoT is not supported, suggesting that changes in the NEI do not significantly affect the IoT.
Full-sample tests are typically employed to examine long-term relationships between variables. However, they rely on an implicit assumption that a single and stable causal structure remains consistent throughout the entire sample period [45]. When the data exhibit structural changes, such estimates may become biased due to the neglect of time-varying parameters. In the case of the IoT and NEI, variations in their relationship across different stages of development may undermine the validity of causality tests based on constant parameter assumptions. To address this issue, this study applies the Sup-F, Ave-F, and Exp-F tests to assess the temporal stability of model parameters and to detect potential structural breaks within the VAR framework. The corresponding results are presented in Table 4.
Table 4 presents the results of the Sup-F, Exp-F, and Ave-F tests, all of which reject the null hypothesis. This indicates that the VAR(s) system composed of the IoT and NEI exhibits significant structural parameter instability, with dynamic variations in their interrelationship over time. Additionally, the Lc statistic confirms the presence of non-stationarity within the parameters of the VAR(s) model. To further explore these dynamic linkages, this study employs a rolling window estimation approach. Using a fixed window length of 24 months, the Granger causality relationship between the IoT and NEI is tested in a sequential manner. Compared with full-sample analysis, this method allows for localized dynamic modeling, which better captures the time-varying characteristics of the causal interactions between variables. Over the sample period from January 2011 to March 2025, a total of 171 rolling sub-samples are generated. The results of the Granger causality tests for each sub-sample are visualized in Figure 2, Figure 3, Figure 4 and Figure 5. The findings reveal that both the direction and strength of the causal effect from the IoT to NEI (and vice versa) exhibit stage-specific fluctuations. By removing the confounding effects of structural breaks, this method uncovers the time-varying mechanisms that are obscured in traditional full-sample analyses, thereby offering more nuanced empirical evidence for dynamic policy evaluation.
As shown in Figure 2, the null hypothesis that “IoT is not a Granger cause of NEI” is rejected at the 10% significance level during three specific periods: March to April 2017, January to July 2020, and February to April 2024. Figure 3 further reveals that during these periods, the impact of the IoT on the NEI is negative. This finding deviates from the full-sample results and is inconsistent with the conclusions of the existing literature [48]. The full-sample results reflect the long-term influence trends between variables, whereas the rolling window sub-sample analysis mainly captures short-term fluctuations and localized anomalies. Particularly during periods of intense market volatility, the statistical relationships may become exaggerated or distorted. The sub-sample analysis reveals that the IoT exerts a significant influence on the NEI, albeit in a negative direction. This finding offers a novel perspective for understanding the dynamic interplay between the two variables.
From March to April 2017, the IoT exerted a negative impact on the NEI. Both sectors had experienced relatively high valuations prior to this period and, as emerging industries, were highly sensitive to market liquidity. The capital outflows in 2017 led to an overall decline in the performance of both the IoT and NEI. From a technological perspective, IoT technologies at that time were still immature, and various issues emerged during application, indirectly affecting the development of the NEI. Specifically, IoT communication technologies were underdeveloped in 2017. Although NB-IoT networks had begun construction, their coverage remained limited, and signal stability was insufficient [49]. For new energy projects, remote monitoring and data transmission via the IoT are critical. The application of the IoT enhances the efficiency of new energy systems and promotes sustainable and low-carbon development. However, unstable networks caused frequent data transmission delays and interruptions, which failed to meet the real-time monitoring and precise management needs of NEI enterprises. For example, wind turbine operational data could not be uploaded in time for analysis, and equipment failures lacked early warning, increasing maintenance costs and downtime losses [50]. These technical challenges weakened NEI companies’ confidence in IoT technologies but also impeded their transition toward intelligent, low-carbon energy systems. Although IBM’s announcement of a collaborative IoT ecosystem at the “Genius of Things” summit on 16 February 2017, along with China’s vigorous promotion of the IoT through the “Internet Plus” strategy, generated positive market sentiment, these developments largely focused on advancing the IoT itself and did not address its low technological maturity. In contrast, the NEI faced challenges such as subsidy reductions and weakening policy support. It was difficult for the sector to overcome these challenges through still-immature IoT solutions [51]. Meanwhile, a short-term speculative boom in the IoT attracted large inflows of capital, which further crowded out investment in the new energy sector. As a result, the NEI suffered from both technological bottlenecks and capital shortages, gradually becoming marginalized in the market, with declining investor interest and public attention. This downturn hindered the progress of renewable energy deployment and delayed the broader transition toward a sustainable low-carbon economic model during that period.
During the period from January to July 2020, the COVID-19 pandemic severely disrupted global logistics, creating extreme external shocks that posed significant risks to the IoT supply chain [52]. In particular, chip shortages halted IoT production and disrupted the synergistic relationship between technology and energy, temporarily walling off the transition toward intelligent, sustainable energy systems. However, this disruption also led to the release of key materials, thereby optimizing the supply chain of the new energy sector. IoT devices rely heavily on rare earths, semiconductors, and other critical resources [53]. As IoT demand declined, these resources were reallocated to the production of new energy equipment—such as silicon used in solar panels and permanent magnets used in wind turbines—alleviating supply chain pressure and reducing production costs in the new energy industry. Meanwhile, funding priorities shifted during the pandemic. Governments and enterprises around the world redirected investments toward medical supply procurement, vaccine development, and social welfare, reducing long-term investments in both new energy and IoT technologies [54]. Given the constraints on long-term investment, the IoT and the new energy industry entered a state of resource competition, leading to a substitution effect during this period, where increases in one coincided with declines in the other. This phenomenon suggests that under extreme external shocks, the collaborative relationship between technology and energy may be temporarily disrupted. In the long run, however, as the pandemic subsides and supply chains recover, the positive synergy between the IoT and NEI is likely to re-emerge.
In 2024, with the continuous improvement in machine learning and deep learning technology, the accuracy of sensors was greatly improved, which lays a solid foundation for the rapid development of AIoT. This is no longer limited to the conceptual level but has been widely and deeply applied in industrial manufacturing, intelligent transportation, smart cities, and many other fields, thus further promoting the development of the IoT [55]. At the same time, the IoT has made remarkable achievements in the field of energy, and IoT applications such as smart grids and energy management platforms have been accelerated. Through the real-time monitoring of energy use, the system can accurately gain insight into the peaks and troughs of energy consumption and then optimize the allocation process with the help of intelligent algorithms, effectively reducing energy waste. The efficient optimization of stock energy correspondingly reduces the demand for new energy capacity under the condition of meeting the same energy demand [56]. The IoT-driven optimization of stock energy shows the alternative potential of making incremental investments in new energy to a certain extent. The rapid development of technology is making the IoT more functional and more stable for energy management. In addition to the impact of improved energy efficiency, the continuous reduction in the cost of IoT devices is also an important factor. The deployment of a large number of low-cost IoT devices makes it more economically feasible to tap into the potential of energy saving through the intelligent transformation of existing energy systems, with less pressure on capital investment than large-scale investment in the construction of new energy projects. Therefore, by combining a variety of factors, the substitution effects of technology made the IoT have a negative impact on the NEI in 2024.
The p-values and estimated coefficients for the NEI and IoT are presented in Figure 4 and Figure 5. As shown in Figure 4, the null hypothesis that “NEI is not a Granger cause of IoT” is rejected at the 10% significance level during four specific periods: July to September 2015, March to July 2016, March to May 2018, and February 2019 to December 2020. According to Figure 5, the NEI exerted a negative influence on the IoT during the period from March to May 2018, while in the other three periods, the impact of the NEI on the IoT was positive.
In 2015, international crude oil prices continued to decline, accelerating downward from July and hitting a new low of below USD 40 per barrel in September. The cost advantage of traditional energy became increasingly evident, severely undermining the market competitiveness of new energy sources. Consequently, market expectations for new energy demand cooled significantly. Meanwhile, from July to September 2015, China’s stock market experienced severe turbulence, prompting investors to withdraw capital from the new energy sector to avoid risk. This large-scale capital outflow left new energy enterprises facing financial constraints, as financing channels were obstructed, and investment projects were forced to scale down. These developments had a direct impact on the demand for IoT devices and solutions. Construction projects in the new energy sector, such as wind farms and solar photovoltaic (PV) power plants, rely heavily on IoT technologies to optimize energy production, reduce operational inefficiencies, and minimize environmental impacts. IoT sensors in wind farms monitor turbine status, wind speed, and wind direction in real time, facilitating optimization through smart control systems. Similarly, PV power plants use IoT technologies for remote monitoring and fault detection [57]. However, due to financial difficulties, many new energy companies postponed or canceled these smart infrastructure projects, resulting in a substantial reduction in IoT procurement orders. In addition, on the R&D front, new energy firms cut spending by suspending collaborative innovation projects with IoT companies, delaying the development of integrated low-carbon technologies. As a result, investments in exploring integrated applications of the IoT and new energy technologies were also curtailed, further shrinking the market space for IoT products and solutions. This contraction in both demand and innovation contributed to the decline in the IoT sector during that period.
From March to July 2016, the NEI had a positive impact on the IoT, primarily driven by the combined forces of the global energy transition and the accelerating wave of digitalization. These efforts encouraged the deployment of IoT technologies in new energy projects as a means to enhance energy efficiency and environmental sustainability. The global pursuit of a low-carbon economy created new momentum for the convergence of digital technologies with green energy systems. From the perspective of technology empowerment, the characteristics of new energy determine the fact that its development is highly dependent on IoT technology. In order to improve energy utilization efficiency and reduce CO2 emissions, distributed photovoltaic power generation projects can realize the real-time monitoring and transmission of power generation data through IoT technologies and realize the implementation of intelligent management systems. New energy has significant decentralized characteristics, such as widely distributed rooftop solar panels, community energy storage stations, etc. This decentralized energy equipment needs to be effectively managed [58]. Through technologies such as sensors and communication networks, the Internet of Things connects these distributed energy devices into an integrated and coordinated system. These innovative applications and developments in the renewable energy sector are inseparable from the support of IoT technologies. Against the backdrop of prior policy incentives and technological advancement, the steady growth of the new energy sector from March to July 2016 further stimulated the development of the IoT industry, accelerating the iteration of related technologies and expanding their application scenarios.
Between February 2019 and December 2020, the NEI had a significant positive impact on IoT development. New energy provided sustainable and low-carbon power for IoT devices, while the integration of 5G, AI, and the IoT offered intelligent solutions for the new energy sector—together forming a key driving force for their synergistic growth [59]. Traditional energy sources face challenges related to pollution, cost, and long-term sustainability, particularly in large-scale applications. In contrast, new energy, with its clean and renewable characteristics, emerged as an ideal energy source for IoT devices. The widespread adoption of new energy alleviated concerns over the power supply for IoT infrastructure, enabling the expansion of the IoT into broader and more remote areas and significantly extending its application boundaries. Furthermore, the deep integration of 5G, AI, and the IoT provided cost-effective and sustainable intelligent solutions for new energy systems and simultaneously reinforced IoT development. With its high speed, low latency, and massive connectivity, 5G allowed for the real-time and stable transmission of large volumes of data from energy devices, facilitating precise remote control and monitoring. This bidirectional empowerment between new energy and IoT technologies during this period fostered a virtuous cycle of growth, jointly driving industrial transformation, accelerating the shift to low-carbon economies, and promoting innovation in sustainable technologies.
However, during the period from March to May 2018, the NEI exerted a negative impact on the IoT, a phenomenon closely linked to the ripple effects of escalating U.S.–China trade tensions. In March 2018, the United States, based on the findings of the Section 301 investigation, imposed tariffs on USD 50 billion worth of Chinese imports, including key new energy products such as photovoltaic modules and lithium batteries. This significantly increased the export costs for Chinese new energy enterprises and weakened their global competitiveness [60]. In addition, U.S. export restrictions on critical raw materials further drove up the production costs of new energy equipment. Essential components such as advanced chips and specialized metal materials, required for the manufacturing of renewable energy systems, faced supply shortages and price surges due to the restrictions. These disruptions affect the stability of new energy sources to power IoT devices, thus hindering the broader adoption of low-carbon and sustainable IoT solutions. Consequently, during this period, the NEI had a negative impact on IoT development.

6. Discussion

This study investigates the bidirectional and dynamic interaction between the IoT and NEI in China. The results from rolling window Granger causality analysis reveal that the relationship between the IoT and NEI is not static but fluctuates over time, exhibiting periods of mutual reinforcement as well as phases of decoupling or even negative influence. This time-varying and nonlinear interaction echoes the theoretical propositions outlined in the literature while also providing fresh empirical evidence on their evolving interplay.

6.1. A Comparison with the Existing Literature

Previous studies have mainly emphasized the unidirectional impact of the IoT on renewable energy systems, highlighting its role in enhancing energy efficiency, system stability, and supply chain intelligence [20,21,22,23,24,25,26]. Our full-sample results align with these findings by showing that in the long run, IoT development significantly promotes NEI performance.
However, in contrast to most previous studies, our findings also reveal phases of reverse impact, indicating that the expansion of the IoT may at times hinder the development of new energy—particularly during periods of intense energy competition or technological immaturity. This supports concerns raised in the literature regarding the energy-intensive nature and environmental costs of IoT infrastructure [26,31], thereby enriching the current understanding of the double-edged nature of digital technologies in the context of the green transition.
Moreover, this paper contributes to the underexplored area of how new energy development affects the IoT, an issue largely overlooked in the literature. While studies such as [34,35,36] acknowledge the role of new energy in powering IoT devices, empirical evidence has been scarce. Our analysis indicates that the NEI also Granger causes the IoT, with the direction and strength of this influence being sensitive to resource availability and grid stability, thereby complementing existing theoretical claims with quantitative backing.

6.2. Limitations and Uncertainties

Despite its contributions, this study has several limitations. Firstly, it relies on the IoT development index and the composite New Energy Index (NEI), which may not fully capture the sectoral heterogeneity within the digital and energy industries. Firm-level or regional panel data could offer more granular insights. Secondly, although the rolling window approach accounts for temporal dynamics, it cannot fully address potential endogeneity or unobserved confounding factors. Thirdly, the empirical framework focuses exclusively on the Chinese context; thus, the findings may not be directly generalizable to countries with different policy environments or energy structures.
Future research could benefit from cross-country comparative analyses, the inclusion of additional mediating variables, and the examination of nonlinear threshold effects or regime-switching dynamics. Moreover, disaggregating the IoT index into industrial, household, and transportation subcomponents may yield more precise insights into sector-specific interactions with new energy development.

7. Conclusions and Implications

This study investigates the relationship between the IoT and NEI. The full-sample Granger causality tests reveal that the IoT has a significant positive impact on the NEI, while the NEI is not a Granger cause of the IoT. The full-sample Granger causality test reflects the long-term impact trend, which is crucial for understanding the broader dynamics of the low-carbon transition. Building upon this, further analysis based on parameter stability tests indicates that the relationship between the IoT and NEI is unstable, highlighting the time-varying and complex nature of their interaction in the context of a rapidly evolving low-carbon economy. To ensure the accuracy of the empirical results, we use a rolling window sub-sample Granger causality estimation, which further confirms the existence of bidirectional causality. The findings suggest that the IoT exerts a significant negative impact on the NEI, whereas the NEI’s influence on the IoT varies over time, showing both positive and negative effects. The negative impact of the IoT on the NEI is mainly due to three aspects. Firstly, the low maturity of IoT technology and the underdeveloped communication infrastructure hinder the advancement of new energy systems. Secondly, under limited resource conditions, the IoT and NEI compete for capital and attention, exhibiting a substitution effect that may undermine the momentum of sustainable development in both sectors. Thirdly, AIoT technology progress promotes the wide application of the IoT in the energy field, improves the efficiency of existing traditional energy, contributes to the substitution potential for incremental investment in new energy, and ultimately has a negative impact on the NEI. The NEI has a mainly positive impact on the IoT, because the decentralized characteristics of new energy require the IoT to achieve intelligent management. New energy provides energy for the IoT and realizes two-way empowerment through the integration of 5G, AI, and the IoT, which contributes to the development of a sustainable low-carbon infrastructure. Due to policy promotion, technology complementarity, and industrial collaboration needs, the NEI has a positive impact on the IoT. The NEI also has a negative impact on the IoT, and the stability of new energy supply needs to be further enhanced. In special periods, it is easy to trigger a supply chain interruption of the new energy industry, thus causing the NEI to have a negative impact on the IoT. These challenges emphasize the need for a resilient and secure supply chain in the transition to a low-carbon economy.
The relationship between the IoT and NEI is complex and dynamic, presenting both opportunities and challenges for fostering a sustainable low-carbon economy. Firstly, given the volatile relationship between the IoT and new energy, policymakers should avoid “one-size-fits-all” policies, especially when striving for a transition to a low-carbon and sustainable energy future. When the maturity of IoT technology is low and affects the development of new energy, the policy should focus on promoting the research and development of IoT technology and communication network construction, such as setting up special funds to support the core technology of the Internet of Things. When there is resource competition between the two, it is necessary to guide the rational allocation of resources through policies, such as formulating industrial collaborative development plans and encouraging enterprises to cooperate in technology research and development, market development, and other aspects. When AIoT technology impacts the incremental investment in new energy, the policy can favor new energy innovation, such as giving tax incentives, subsidies, and other support to promote the upgrading of the new energy industry, which would accelerate the shift to a low-carbon energy system. Secondly, enterprises and industry organizations should pay attention to the potential of collaborative development between the two sectors in driving sustainable development. New energy enterprises can actively use IoT technology to realize the intelligent management of distributed energy and improve operational efficiency. At the same time, IoT enterprises can rely on the advantages of the clean energy supply of new energy to expand application scenarios, especially in remote areas or fields with high requirements for energy sustainability. The two sides can jointly explore innovative cooperation models, such as establishing a joint laboratory to carry out application research on 5G, AI, and IoT fusion technologies in the field of new energy, so as to achieve two-way empowerment and foster a low-carbon transformation. Thirdly, it is critical to continue to promote innovation in IoT and new energy technologies. In view of the low maturity of IoT technology, investment in research and development should be increased to improve the stability and coverage of communication networks. In the field of new energy, efforts will be made to solve the problem of energy supply stability, such as developing advanced energy storage technology and optimizing energy transmission networks. In addition, cross-field technology integration and innovation should be encouraged while using AIoT technology to improve energy management efficiency, and we should also pay attention to tapping into its potential for coordinated development with new energy to avoid excessive substitution and ensure a balanced, sustainable low-carbon economy. Finally, special periods such as trade frictions and epidemics are likely to lead to supply chain disruption in the new energy industry, thus affecting the development of the IoT. Enterprises and governments should establish and improve the supply chain risk early warning and response mechanism and strengthen the independent control ability of key raw materials and technologies. For example, new energy enterprises can expand raw material supply channels and establish strategic reserves. The government can issue policies to support local enterprises to develop core technologies, reduce external dependence, and ensure the stable operation of the industrial supply chain, ultimately fostering a resilient and low-carbon economy.

Author Contributions

Conceptualization, Y.C. and Y.H.; methodology, J.L.; software, Y.H.; validation, Y.C., Y.H. and J.L.; formal analysis, Y.C.; investigation, Y.H.; resources, J.L.; data curation, Y.H.; writing—original draft preparation, Y.H.; writing—review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant 72172018) and the Chinese Academy of Engineering Strategic Research and Consulting Program (Grant 2025NMZD-01).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data results in this paper were calculated and analyzed using EViews13 and R 4.3.2 software. The datasets generated or used during this study may be requested from the corresponding author upon request.

Acknowledgments

The authors sincerely thank Cole Rees of Swansea University, Wales, UK, for his valuable comments and constructive suggestions, which significantly enhanced the quality of this paper. We sincerely thank the anonymous reviewers for their insightful comments and suggestions. We are also especially grateful to Jaime Ortiz, the guest editor of this Special Issue, for his professional handling of the manuscript and valuable editorial support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The trends in the IoT and NEI. Notes: The solid line represents the trend in the IoT; the dashed line indicates the trend in the NEI.
Figure 1. The trends in the IoT and NEI. Notes: The solid line represents the trend in the IoT; the dashed line indicates the trend in the NEI.
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Figure 2. Examining the null hypothesis that the IoT is not a Granger cause of the NEI. Notes: This research counts p-values by using 10,000 bootstrap repetitions. The solid line represents the bootstrap p-values, and the dashed line indicates that the p-value is 0.1.
Figure 2. Examining the null hypothesis that the IoT is not a Granger cause of the NEI. Notes: This research counts p-values by using 10,000 bootstrap repetitions. The solid line represents the bootstrap p-values, and the dashed line indicates that the p-value is 0.1.
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Figure 3. The coefficients of the influence from the IoT to the NEI. Note: The shadow represents the interval where the IoT has significant Granger causality with respect to the NEI.
Figure 3. The coefficients of the influence from the IoT to the NEI. Note: The shadow represents the interval where the IoT has significant Granger causality with respect to the NEI.
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Figure 4. Examining the null hypothesis that the NEI is not a Granger cause of the IoT. Notes: This research counts p-values by using 10,000 bootstrap repetitions. The solid line indicates the bootstrap p-values, and the dashed line indicates that the p-value is 0.1.
Figure 4. Examining the null hypothesis that the NEI is not a Granger cause of the IoT. Notes: This research counts p-values by using 10,000 bootstrap repetitions. The solid line indicates the bootstrap p-values, and the dashed line indicates that the p-value is 0.1.
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Figure 5. The coefficients of the influence from the NEI to the IoT. Note: The shadow represents the interval where the NEI has significant Granger causality with respect to the IoT.
Figure 5. The coefficients of the influence from the NEI to the IoT. Note: The shadow represents the interval where the NEI has significant Granger causality with respect to the IoT.
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Table 1. Descriptive statistics for IoT and NEI.
Table 1. Descriptive statistics for IoT and NEI.
IoTNEI
Observations171171
Mean2462.3761176.198
Median2721.5011118.452
Maximum4457.7312510.484
Minimum651.6100479.7256
Standard Deviation944.1179482.7832
Skewness−0.4560480.854343
Kurtosis2.1901223.094968
Jarque–Bera10.6007220.86646 *
Probability0.0049900.000029
Notes: * denotes significance at the 10% level.
Table 2. Unit root tests.
Table 2. Unit root tests.
VariablesADFPPDF-GLS
IoT−0.021 (0)−0.021 [4]−0.003 (0)
∆IoT−0.958 (0) ***−0.958 [5] ***−0.147 (6) *
NEI−0.026 (0)−0.026 [6]−0.023 (0)
∆NEI−0.839 (0) ***−0.838 [11] ***−0.485 (0) ***
Notes: *** and * denote significance at the 1% and 10% levels, respectively. ( ) = lag length; [ ] = bandwidth.
Table 3. Outcomes of bootstrap full-sample method.
Table 3. Outcomes of bootstrap full-sample method.
H0: IoT Is Not the Granger Cause of the NEIH0: NEI Is Not the Granger Cause of the IoT
Statisticp-ValueStatisticp-Value
9.1290.065 *6.2730.122
Notes: * denotes significance at the 10% level.This research counts p-values by using 10,000 bootstrap repetitions.
Table 4. Outcomes of parameter stability techniques.
Table 4. Outcomes of parameter stability techniques.
TestsIoTNEIVAR (s) Process
Statisticsp-ValuesStatisticsp-ValuesStatisticsp-Values
Sup-F25.133 ***0.00023.245 ***0.00122.7210.019
Ave-F11.113 ***0.0018.101 **0.01111.0640.030
Exp-F8.569 ***0.0027.414 ***0.0047.2550.039
Lc 1.410 *0.078
Note: ***, **, * represent significance at 1%, 5%, and 10% levels.
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Chen, Y.; Hou, Y.; Lyu, J. Integrating the IoT and New Energy to Promote a Sustainable Low-Carbon Economy. Sustainability 2025, 17, 6755. https://doi.org/10.3390/su17156755

AMA Style

Chen Y, Hou Y, Lyu J. Integrating the IoT and New Energy to Promote a Sustainable Low-Carbon Economy. Sustainability. 2025; 17(15):6755. https://doi.org/10.3390/su17156755

Chicago/Turabian Style

Chen, Yan, Yuqi Hou, and Jiayi Lyu. 2025. "Integrating the IoT and New Energy to Promote a Sustainable Low-Carbon Economy" Sustainability 17, no. 15: 6755. https://doi.org/10.3390/su17156755

APA Style

Chen, Y., Hou, Y., & Lyu, J. (2025). Integrating the IoT and New Energy to Promote a Sustainable Low-Carbon Economy. Sustainability, 17(15), 6755. https://doi.org/10.3390/su17156755

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